Method, apparatus, and computer program product for performance analytics determining play models and outputting events based on real-time data for proximity and movement of objects

ABSTRACT

Systems, methods, apparatuses, and computer readable media are disclosed for providing analytics using real time data on movement and proximity of tagged objects for determining play models and outputting events. In one embodiment, a method is provided for determining play data that at least includes correlating at least one tag to a participant; receiving blink data transmitted by the at least one tag; and determining tag location data based on the blink data. The method further includes receiving participant role data; comparing the tag location data to participant dynamics/kinetics models based at least in part on the participant role data; determining participant location data based on the comparing the tag location data to the participant dynamics/kinetics models.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/942,585, filed Jul. 15, 2013, which claims priority from and thebenefit of the filing date of U.S. Provisional Patent Application No.61/831,990 filed Jun. 6, 2013, the contents of each of which areincorporated by reference in their entirety herein.

FIELD

Embodiments discussed herein are related to radio frequency locatingand, more particularly, to systems, methods, apparatuses, computerreadable media and other means for providing performance analytics.

BACKGROUND

Producing analysis of performance for sports events and/or teams isgenerally a resource intensive process often involving experiencedindividuals manually reviewing games or recordings of games to compileevents and statistics for a game and the participants. Such analysis maybe error prone as it requires reviewing a large number of participantsmoving among complex formations at each moment of a game.

A number of deficiencies and problems associated with providingperformance analytics are identified herein. Through applied effort,ingenuity, and innovation, exemplary solutions to many of theseidentified problems are embodied by the present invention, which isdescribed in detail below.

BRIEF SUMMARY

Systems, methods, apparatuses, and computer readable media are disclosedfor providing real-time collection and analysis of participant (e.g.,player) performance, events, and statistics during a sporting event orother group activity using a locating system, such as a radio frequencylocating system, as herein described.

Embodiments of the present invention may provide for automaticrecognition of formations, plays, and events during a sporting eventthrough the processing of real time (or near real time) data regardinglocation, change in location, change in acceleration, orientation,sensor data, or the like, for participants that comprise a team or areotherwise associated with a sporting event or other group activity andhow such data fits models that define the formations, plays, and events.Once such formations, plays, and events have been defined or identifiedthey may be used to operate, control, or drive analytics or controlsystems such as, without limitation, visualization systems, gameoperations systems, camera control systems, team analytics systems,league analytics systems, statistics systems, and XML feed/IM feedsystems.

In one embodiment, a method for monitoring a participant is provided,the method comprising correlating at least one tag to the participant,receiving blink data transmitted by the at least one tag, determiningtag location data based on the blink data, comparing the tag locationdata to participant dynamics/kinetics models based at least in part onthe tag location data, and determining participant location data basedon the comparing the tag location data to the participantdynamics/kinetics models.

In some embodiments, the tag comprises an ultra-wideband (UWB)transmitter. In some embodiments, determining tag location datacomprises determining a first tag derived data component and a secondtag derived data component, and wherein the determining participantlocation data comprises determining participant location data byassigning a first weight to the first tag derived data component and asecond weight to the second tag derived data component.

In some embodiments, the determining participant location data comprisesassigning the first weight to the first tag derived data component andthe second weight to the second tag derived data component at a firsttime period, and further comprises assigning a third weight to the firsttag derived data component and assigning a fourth weight to the secondtag derived data component at a second time period. In some embodiments,the method may further comprise receiving field data, receivingparticipant role data, comparing the participant location data toformation models based at least in part on the participant role data andthe field data, and determining formation data based on the comparingthe participant location data to the formation models.

In some embodiments, the participant location data comprises a firstparticipant component and a second participant component, and whereinthe determining formation data comprises determining formation data byassigning a first weight to the first participant component and a secondweight to the second participant component. In some embodiments, thedetermining participant location data comprises assigning the firstweight to the first participant component and the second weight to thesecond participant component at a first time period, and furthercomprises assigning a third weight to the first participant component ata second time period and assigning a fourth weight to the secondparticipant component at the second time period.

In some embodiments, the participant dynamics/kinetics models compriselocation history data for the participant. In some embodiments, themethod may further comprise updating the participant role data based onthe participant location data. In some embodiments, the method mayfurther comprise updating the dynamics/kinetics models based on theparticipant location data. In some embodiments, the method may furthercomprise updating the formation models based on the formation data. Insome embodiments, the UWB transmitter is configured to transmit blinkdata comprising a plurality of time of arrival (TOA) timing pulses. Insome embodiments, the UWB transmitter is configured to transmit blinkdata comprising information packets sized to approximately 112 bits.

In another embodiment, an apparatus for monitoring a participant isprovided, the apparatus comprising at least one processor and at leastone memory including computer program instructions, the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus tocorrelate at least one tag to the participant, receive blink datatransmitted by the at least one tag, determine tag location data basedon the blink data, receive participant role data, compare the taglocation data to participant dynamics/kinetics models based at least inpart on the participant role data, and determine participant locationdata based on the comparing the tag location data to the participantdynamics/kinetics models.

In some embodiments, the tag comprises an ultra-wideband (UWB)transmitter. In some embodiments, determining tag location datacomprises determining a first tag derived data component and a secondtag derived data component, and wherein the determining participantlocation data comprises determining participant location data byassigning a first weight to the first tag derived data component and asecond weight to the second tag derived data component.

In some embodiments, determining participant location data comprisesassigning the first weight to the first tag derived data component andthe second weight to the second tag derived data component at a firsttime period, and further comprises assigning a third weight to the firsttag derived data component and assigning a fourth weight to the secondtag derived data component at a second time period. In some embodiments,the at least one memory and the computer program instructions may befurther configured to, in cooperation with the at least one processor,cause the apparatus to receive field data, compare the participantlocation data to formation models based at least in part on theparticipant role data and the field data, and determine formation databased on the comparing the participant location data to the formationmodels.

In some embodiments, the participant location data comprises a firstparticipant component and a second participant component, and whereinthe determining formation data comprises determining formation data byassigning a first weight to the first participant component and a secondweight to the second participant component. In some embodiments, thedetermining participant location data comprises assigning the firstweight to the first participant component and the second weight to thesecond participant component at a first time period, and furthercomprises assigning a third weight to the first participant component ata second time period and assigning a fourth weight to the secondparticipant component at the second time period. In some embodiments,the participant dynamics/kinetics models comprise location history datafor the participant.

In some embodiments, the at least one memory and the computer programinstructions are further configured to, in cooperation with the at leastone processor, cause the apparatus to update the participant role databased on the participant location data. In some embodiments, the atleast one memory and the computer program instructions are furtherconfigured to, in cooperation with the at least one processor, cause theapparatus to update the dynamics/kinetics models based on theparticipant location data. In some embodiments, the at least one memoryand the computer program instructions are further configured to, incooperation with the at least one processor, cause the apparatus toupdate the formation models based on the formation data. In someembodiments, the blink data comprises a plurality of time of arrival(TOA) timing pulses. In some embodiments, the blink data comprisesinformation packets sized to approximately 112 bits.

In another embodiment, a computer program product for monitoring aparticipant may be provided, the computer program product may comprise anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to correlate atleast one tag to the participant, receive blink data transmitted by theat least one tag, determine tag location data based on the blink data,receive participant role data, compare the tag location data toparticipant dynamics/kinetics models based at least in part on theparticipant role data, and determine participant location data based onthe comparing the tag location data to the participant dynamics/kineticsmodels. In some embodiments, the tag comprises an ultra-wideband (UWB)transmitter.

In some embodiments, determining tag location data comprises determininga first tag derived data component and a second tag derived datacomponent, and wherein the determining participant location datacomprises determining participant location data by assigning a firstweight to the first tag derived data component and a second weight tothe second tag derived data component. In some embodiments, determiningparticipant location data comprises assigning the first weight to thefirst tag derived data component and the second weight to the second tagderived data component at a first time period, and further comprisesassigning a third weight to the first tag derived data component andassigning a fourth weight to the second tag derived data component at asecond time period.

In some embodiments, the computer program instructions may be furtherconfigured to receive field data, compare the participant location datato formation models based at least in part on the participant role dataand the field data, and determine formation data based on the comparingthe participant location data to the formation models. In someembodiments, the participant location data comprises a first participantcomponent and a second participant component, and wherein thedetermining formation data comprises determining formation data byassigning a first weight to the first participant component and a secondweight to the second participant component.

In some embodiments, the determining participant location data comprisesassigning the first weight to the first participant component and thesecond weight to the second participant component at a first timeperiod, and further comprises assigning a third weight to the firstparticipant component at a second time period and assigning a fourthweight to the second participant component at the second time period. Insome embodiments, the participant dynamics/kinetics models compriselocation history data for the participant. In some embodiments, thecomputer program instructions may be further configured to update theparticipant role data based on the participant location data. In someembodiments, the computer program instructions may be further configuredto update the dynamics/kinetics models based on the participant locationdata. In some embodiments, the computer program instructions may befurther configured to update the formation models based on the formationdata. In some embodiments, the blink data comprises a plurality of timeof arrival (TOA) timing pulses. In some embodiments, the blink datacomprises information packets sized to approximately 112 bits.

In one embodiment, a method is provided for monitoring a participantthat at least includes correlating at least one tag to the participant,receiving blink data transmitted by the at least one tag, determiningtag location data based on the blink data, correlating a sensor to theparticipant, and receiving sensor derived data. The method furtherincludes receiving participant role data, comparing the tag locationdata to participant dynamics/kinetics models based at least in part onthe participant role data, and determining the participant location databased on comparing the tag location data and the sensor derived data tothe participant dynamics/kinetics models.

In some embodiments, the sensor may comprise one or more of anaccelerometer, a magnetometer, and a time-of-flight sensor. In someembodiments, determining participant location data comprises determiningparticipant location data by assigning a first weight to the taglocation data and a second weight to the sensor derived data.

In some embodiments, determining participant location data comprisesassigning a first weight to the tag location data and a second weight tothe sensor derived data at a first time period, and assigning a thirdweight to the tag location data at a second time period and a fourthweight to the sensor derived data at the second time period.

In some embodiments, determining the tag location data comprisesdetermining a first tag derived data component and a second tag deriveddata component, wherein the sensor derived data comprises a first sensorderived data component and a second sensor derived data component,wherein the determining participant location data comprises determiningparticipant location data by assigning a first weight to the first tagderived data component, a second weight to the second tag derived datacomponent, a third weight to the first sensor derived data component,and a fourth weight to the second sensor derived data component.

In some embodiments, determining participant location data comprisesassigning at a first time period the first weight to the first tagderived data component, the second weight to the second tag derived datacomponent, the third weight to the first sensor derived data component,and the fourth weight to the second sensor derived data component, andat a second time period assigning a fifth weight to the first tagderived data component, a sixth weight to the second tag derived datacomponent, a seventh weight to the first sensor derived data component,and an eighth weight to the second sensor derived data component.

In some embodiments, the method may further comprise receiving fielddata, comparing the participant location data to formation models basedat least in part on the participant role data and the field data, anddetermining formation data based on the comparing the participantlocation data to the formation models.

In some embodiments, the participant location data may comprise a firstparticipant component and a second participant component, anddetermining formation data may comprise determining formation data byassigning a first weight to the first participant component and a secondweight to the second participant component.

In some embodiments, determining participant location data comprisesassigning the first weight to the first participant component and thesecond weight to the second participant component at a first timeperiod, and further comprises assigning a third weight to the firstparticipant component at a second time period and assigning a fourthweight to the second participant component at the second time period.

In some embodiments, the method may further comprise comparing theformation data and participant location data to play models, anddetermining play data based on the comparing the formation data andparticipant location data to the play models.

In some embodiments, the formation data comprises a first formationcomponent and a second formation component, and determining play datamay comprise determining play data by assigning a first weight to thefirst formation component and a second weight to the second formationcomponent.

In some embodiments, determining play data comprises assigning the firstweight to the first formation component and the second weight to thesecond formation component at a first time period, and further comprisesassigning a third weight to the first formation component at a secondtime period and assigning a fourth weight to the second formationcomponent at the second time period.

In some embodiments, the method may further comprise determining thatthe participant is a player, and receiving the tag location data to aplayer dynamics engine, wherein the participant role data is player roledata received to the player dynamics engine, wherein the participantdynamics/kinetics models are player dynamics/kinetics models, andwherein the player dynamics engine compares the tag location data to theplayer dynamics/kinetics models based at least in part on the playerrole data.

In some embodiments, the method may further comprise determining thatthe participant is an official, and receiving the tag location data toan official dynamics engine, wherein the participant role data isofficial role data received to the official dynamics engine, wherein theparticipant dynamics/kinetics models are official dynamics/kineticsmodels, and wherein the official dynamics engine compares the taglocation data to the official dynamics/kinetics models based at least inpart on the official role data.

In some embodiments, the method may further comprise determining thatthe participant is a ball, and receiving the tag location data to a ballengine, wherein the participant role data is ball role data received tothe ball engine, wherein the participant dynamics/kinetics models areball dynamics/kinetics models, and wherein the ball engine compares thetag location data to the ball dynamics/kinetics models based at least inpart on the ball role data.

In some embodiments, the method may further comprise determining thatthe participant is a field marker, and receiving the tag data to a fieldmarker engine, wherein the participant role data is field marker roledata received to the field marker engine, wherein the participantdynamics/kinetics models are field marker dynamics/kinetics models, andwherein the field marker engine compares the tag location data to thefield marker dynamics/kinetics models based at least in part on thefield marker role data.

In some embodiments, the sensor derived data comprises time-of-flightsensor data and the method may further comprise correlating thetime-of-flight sensor data to the participant. In some embodiments, thesensor derived data comprises time-of-flight sensor data and the methodmay further comprise assigning a first weight to the tag location dataand a second weight to the time-of-flight sensor data.

In another embodiment, an apparatus is provided comprising at least oneprocessor and at least one memory including computer programinstructions, the at least one memory and the computer programinstructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to correlate at least one tag to theparticipant, receive blink data transmitted by the at least one tag,determine tag location data based on the blink data, correlate a sensorto the participant, receive sensor derived data, receive participantrole data, compare the tag location data to participantdynamics/kinetics models based at least in part on the participant roledata, and determine the participant location data based on comparing thetag location data and the sensor derived data to the participantdynamics/kinetics models.

In some embodiments, the sensor comprises one or more of anaccelerometer, a magnetometer, and a time-of-flight sensor. In someembodiments, determining participant location data comprises determiningparticipant location data by assigning a first weight to the taglocation data and a second weight to the sensor derived data.

In some embodiments, determining participant location data comprisesassigning a first weight to the tag location data and a second weight tothe sensor derived data at a first time period, and assigning a thirdweight to the tag location data at a second time period and a fourthweight to the sensor derived data at the second time period.

In some embodiments, determining the tag location data comprisesdetermining a first tag derived data component and a second tag deriveddata component, wherein the sensor derived data comprises a first sensorderived data component and a second sensor derived data component,wherein the determining participant location data comprises determiningparticipant location data by assigning a first weight to the first tagderived data component, a second weight to the second tag derived datacomponent, a third weight to the first sensor derived data component,and a fourth weight to the second sensor derived data component.

In some embodiments, determining participant location data comprisesassigning at a first time period the first weight to the first tagderived data component, the second weight to the second tag derived datacomponent, the third weight to the first sensor derived data component,and the fourth weight to the second sensor derived data component, andat a second time period assigning a fifth weight to the first tagderived data component, a sixth weight to the second tag derived datacomponent, a seventh weight to the first sensor derived data component,and an eighth weight to the second sensor derived data component.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus toreceive field data, compare the participant location data to formationmodels based at least in part on the participant role data and the fielddata, and determine formation data based on the comparing theparticipant location data to the formation models.

In some embodiments, the participant location data comprises a firstparticipant component and a second participant component, and whereinthe determining formation data comprises determining formation data byassigning a first weight to the first participant component and a secondweight to the second participant component.

In some embodiments, determining participant location data comprisesassigning the first weight to the first participant component and thesecond weight to the second participant component at a first timeperiod, and further comprises assigning a third weight to the firstparticipant component at a second time period and assigning a fourthweight to the second participant component at the second time period.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus tocompare the formation data and participant location data to play models,and determine play data based on the comparing the formation data andparticipant location data to the play models.

In some embodiments, the formation data comprises a first formationcomponent and a second formation component, and wherein the determiningplay data comprises determining play data by assigning a first weight tothe first formation component and a second weight to the secondformation component.

In some embodiments, determining play data comprises assigning the firstweight to the first formation component and the second weight to thesecond formation component at a first time period, and further comprisesassigning a third weight to the first formation component at a secondtime period and assigning a fourth weight to the second formationcomponent at the second time period.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus todetermine that the participant is a player, and receive the tag locationdata to a player dynamics engine, wherein the participant role data isplayer role data received to the player dynamics engine, wherein theparticipant dynamics/kinetics models are player dynamics/kineticsmodels, and wherein the player dynamics engine compares the tag locationdata to the player dynamics/kinetics models based at least in part onthe player role data.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus todetermine that the participant is an official, and receive the taglocation data to an official dynamics engine, wherein the participantrole data is official role data received to the official dynamicsengine, wherein the participant dynamics/kinetics models are officialdynamics/kinetics models, and wherein the official dynamics enginecompares the tag location data to the official dynamics/kinetics modelsbased at least in part on the official role data.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus todetermine that the participant is a ball, and receive the tag locationdata to a ball engine, wherein the participant role data is ball roledata received to the ball engine, wherein the participantdynamics/kinetics models are ball dynamics/kinetics models, and whereinthe ball engine compares the tag location data to the balldynamics/kinetics models based at least in part on the ball role data.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus todetermine that the participant is a field marker, and receive the tagdata to a field marker engine, wherein the participant role data isfield marker role data received to the field marker engine, wherein theparticipant dynamics/kinetics models are field marker dynamics/kineticsmodels, and wherein the field marker engine compares the tag locationdata to the field marker dynamics/kinetics models based at least in parton the field marker role data.

In some embodiments, the sensor derived data may comprise time-of-flightsensor data and the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus tocorrelate the time-of-flight sensor data to the participant. In someembodiments, the sensor derived data may comprise time-of-flight sensordata and the apparatus may further comprise the at least one memory andthe computer program instructions configured to, in cooperation with theat least one processor, cause the apparatus to assign a first weight tothe tag location data and a second weight to the time-of-flight sensordata.

In another embodiment, a computer program product is provided formonitoring a participant, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to correlate atleast one tag to the participant, receive blink data transmitted by theat least one tag, determine tag location data based on the blink data,correlate a sensor to the participant, receive sensor derived data,receive participant role data, compare the tag location data toparticipant dynamics/kinetics models based at least in part on theparticipant role data, and determine the participant location data basedon comparing the tag location data and the sensor derived data to theparticipant dynamics/kinetics models.

In some embodiments, the sensor may comprise one or more of anaccelerometer, a magnetometer, and a time-of-flight sensor. In someembodiments, the computer program product may further comprise thecomputer program instructions at least configured to receive field data,compare the participant location data to formation models based at leastin part on the participant role data and the field data, and determineformation data based on the comparing the participant location data tothe formation models.

In some embodiments, the computer program product may further comprisethe computer program instructions at least configured to compare theformation data and participant location data to play models, anddetermine play data based on the comparing the formation data andparticipant location data to the play models.

In some embodiments, the computer program product may further comprisethe computer program instructions at least configured to determine thatthe participant is a player, and receive the tag location data to aplayer dynamics engine, wherein the participant role data is player roledata received to the player dynamics engine, wherein the participantdynamics/kinetics models are player dynamics/kinetics models, andwherein the player dynamics engine compares the tag location data to theplayer dynamics/kinetics models based at least in part on the playerrole data.

In some embodiments, the computer program product may further comprisethe computer program instructions at least configured to determine thatthe participant is an official, and receive the tag location data to anofficial dynamics engine, wherein the participant role data is officialrole data received to the official dynamics engine, wherein theparticipant dynamics/kinetics models are official dynamics/kineticsmodels, and wherein the official dynamics engine compares the taglocation data to the official dynamics/kinetics models based at least inpart on the official role data.

In some embodiments, the computer program product may further comprisethe computer program instructions at least configured to determine thatthe participant is a ball, and receive the tag location data to a ballengine, wherein the participant role data is ball role data received tothe ball engine, wherein the participant dynamics/kinetics models areball dynamics/kinetics models, and wherein the ball engine compares thetag location data to the ball dynamics/kinetics models based at least inpart on the ball role data.

In some embodiments, the computer program product may further comprisethe computer program instructions at least configured to determine thatthe participant is a field marker, and receive the tag data to a fieldmarker engine, wherein the participant role data is field marker roledata received to the field marker engine, wherein the participantdynamics/kinetics models are field marker dynamics/kinetics models, andwherein the field marker engine compares the tag location data to thefield marker dynamics/kinetics models based at least in part on thefield marker role data.

In some embodiments, the sensor derived data may comprise time-of-flightsensor data and the computer program product may further comprise thecomputer program instructions at least configured to correlate thetime-of-flight sensor data to the participant. In some embodiments, thesensor derived data may comprise time-of-flight sensor data and thecomputer program product may further comprise the computer programinstructions at least configured to assign a first weight to the taglocation data and a second weight to the time-of-flight sensor data.

In another embodiment, a system is provided for monitoring a participantcomprising one or more tags and an apparatus. The apparatus may compriseat least one processor and at least one memory including computerprogram instructions, the at least one memory and the computer programinstructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to correlating at least one tag to theparticipant, receiving blink data transmitted by the at least one tag,determining tag location data based on the blink data, correlating asensor to the participant, receiving sensor derived data, receivingparticipant role data, comparing the tag location data to participantdynamics/kinetics models based at least in part on the participant roledata, and determining the participant location data based on comparingthe tag location data and the sensor derived data to the participantdynamics/kinetics models.

In one embodiment, a method is provided for monitoring a participantthat at least includes correlating at least one tag to the participant,receiving blink data transmitted by the at least one tag, anddetermining tag location data based on the blink data. The methodfurther includes receiving participant role data, comparing the taglocation data to participant dynamics/kinetics models based at least inpart on the participant role data, and determining participant locationdata based on the comparing the tag location data to the participantdynamics/kinetics models.

In some embodiments, the method may further comprise receiving fielddata, comparing the participant location data to formation models basedat least in part on the participant role data and the field data, anddetermining formation data based on the comparing the participantlocation data to the formation models, and determining a probable playbased on comparing the formation data to play models. In someembodiments, determining the probable play is based on generating aprobable play ranked list.

In another embodiment, a method is provided for monitoring a participantthat at least includes correlating at least one tag to the participant,receiving blink data transmitted by the at least one tag, determiningtag location data based on the blink data, receiving participant roledata, receiving weather data, comparing the tag location data toparticipant dynamics/kinetics models based at least in part on theparticipant role data, determining participant location data based onthe comparing the tag location data to the participant dynamics/kineticsmodels, and updating stored weather-adjusted participant location databased on the participant location data and the weather data.

In some embodiments, the method may further comprise determiningparticipant performance information based on comparing the participantlocation data and the weather data to the stored weather-adjustedparticipant location data.

In another embodiment, a method is provided for evaluating a player, themethod comprising correlating at least one tag to the player, receivingblink data transmitted by the at least one tag, determining tag locationdata based on the blink data, receiving player role data, receivingweather data, comparing the tag location data to playerdynamics/kinetics models based at least in part on the player role data,determining player location data based on the comparing the tag locationdata to the player dynamics/kinetics models, and determining playerperformance information based on comparing the player location data andthe weather data to stored weather-adjusted player location data.

In another embodiment, an apparatus is provided for monitoring aparticipant comprising at least one processor and at least one memoryincluding computer program instructions, the at least one memory and thecomputer program instructions configured to, in cooperation with the atleast one processor, cause the apparatus to correlate at least one tagto the participant, receive blink data transmitted by the at least onetag, determine tag location data based on the blink data, receiveparticipant role data, compare the tag location data to participantdynamics/kinetics models based at least in part on the participant roledata, and determine participant location data based on the comparing thetag location data to the participant dynamics/kinetics models.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus toreceive field data, compare the participant location data to formationmodels based at least in part on the participant role data and the fielddata, determine formation data based on the comparing the participantlocation data to the formation models, and determine a probable playbased on comparing the formation data to play models. In someembodiments, determining the probable play is based on generating aprobable play ranked list.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus toreceive field data, compare the participant location data to formationmodels based at least in part on the participant role data and the fielddata, and determine formation data based on the comparing theparticipant location data to the formation models.

In another embodiment, an apparatus is provided for monitoring aparticipant comprising at least one processor and at least one memoryincluding computer program instructions, the at least one memory and thecomputer program instructions configured to, in cooperation with the atleast one processor, cause the apparatus to correlate at least one tagto the participant, receive blink data transmitted by the at least onetag, determine tag location data based on the blink data, receiveparticipant role data, receive weather data, compare the tag locationdata to participant dynamics/kinetics models based at least in part onthe participant role data, determine participant location data based onthe comparing the tag location data to the participant dynamics/kineticsmodels, and update stored weather-adjusted participant location databased on the participant location data and the weather data.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus todetermine participant performance information based on comparing theparticipant location data and the weather data to the storedweather-adjusted participant location data.

In another embodiment, an apparatus is provided for evaluating a playercomprising at least one processor and at least one memory includingcomputer program instructions, the at least one memory and the computerprogram instructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to correlate at least one tag to theplayer, receive blink data transmitted by the at least one tag,determine tag location data based on the blink data, receive player roledata, receive weather data, compare the tag location data to playerdynamics/kinetics models based at least in part on the player role data,determine player location data based on the comparing the tag locationdata to the player dynamics/kinetics models, and determine playerperformance information based on comparing the player location data andthe weather data to stored weather-adjusted player location data.

In another embodiment, a computer program product is provided formonitoring a participant, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to correlate atleast one tag to the participant, receive blink data transmitted by theat least one tag, determine tag location data based on the blink data,receive participant role data, compare the tag location data toparticipant dynamics/kinetics models based at least in part on theparticipant role data, and determine participant location data based onthe comparing the tag location data to the participant dynamics/kineticsmodels.

In some embodiments, the computer program product may further comprisethe computer program instructions at least configured to receive fielddata, compare the participant location data to formation models based atleast in part on the participant role data and the field data, anddetermine formation data based on the comparing the participant locationdata to the formation models. In some embodiments, the computer programproduct may further comprise the computer program instructions at leastconfigured to determine a probable play based on comparing the formationdata to play models. In some embodiments, determining the probable playis based on generating a probable play ranked list.

In another embodiment, a computer program product is provided formonitoring a participant, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to correlate atleast one tag to the participant, receive blink data transmitted by theat least one tag, determine tag location data based on the blink data,receive participant role data, receive weather data, compare the taglocation data to participant dynamics/kinetics models based at least inpart on the participant role data, determine participant location databased on the comparing the tag location data to the participantdynamics/kinetics models, and update stored weather-adjusted participantlocation data based on the participant location data and the weatherdata.

In some embodiments, the computer program product may further comprisethe computer program instructions at least configured to determineparticipant performance information based on comparing the participantlocation data and the weather data to the stored weather-adjustedparticipant location data.

In another embodiment, a computer program product is provided forevaluating a player, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to correlate atleast one tag to the player, receive blink data transmitted by the atleast one tag, determine tag location data based on the blink data,receive player role data, receive weather data, compare the tag locationdata to player dynamics/kinetics models based at least in part on theplayer role data, determine player location data based on the comparingthe tag location data to the player dynamics/kinetics models, anddetermine player performance information based on comparing the playerlocation data and the weather data to stored weather-adjusted playerlocation data.

In one embodiment, a method is provided for generating formation datathat at least includes receiving participant location data determinedbased on tag derived data and participant role data; receiving fielddata; comparing the participant location data to formation models basedat least in part on the participant location data, the participant roledata, and the field data; and determining formation data based on thecomparing the participant location data to the formation models.

In some embodiments, the method may further include comparing theparticipant location data to play models based at least in part on theparticipant role data, the field data, and the formation data; anddetermining play data based on the comparing the participant locationdata and the formation data to the play models. In some embodiments, themethod may further include updating the formation models based on theformation data.

In another embodiment, a method is provided for monitoring a participantthat at least includes correlating at least one tag to the participant;correlating at least one sensor to the participant; receiving blink dataassociated with the at least one tag; receiving sensor derived dataassociated with the at least one sensor; determining tag location databased at least on the blink data; comparing the tag location data andthe sensor derived data to participant dynamics/kinetics models;determining participant location data based on the comparing the taglocation data and the sensor derived data to the participantdynamics/kinetics models; and determining participant role data based onthe determining participant location data. In some embodiments, themethod may further include updating the dynamics/kinetics models basedon the participant location data.

In another embodiment, a method is provided for generating play datathat at least includes receiving participant location data determinedbased on tag location data, sensor derived data, and participant roledata; receiving formation data and field data; comparing the participantlocation data and formation data to play models based at least in parton the participant role data and the field data; and determining playdata based on the comparing the participant location data and theformation data to the play models. In some embodiments, the method mayfurther include updating the play models based on the play data.

In another embodiment, a method is provided for generating sportingevent analytics comprising receiving participant location data;receiving participant role data; receiving formation data; receivingplay data; determining output events based at least in part on theparticipant location data, the participant role data, the formationdata, and the play data; storing the output events in a data store; andproviding at least some of the output events to one or more analyticssystems or control systems.

In another embodiment, an apparatus is provided for generating formationdata comprising at least one processor and at least one memory includingcomputer program instructions, the at least one memory and the computerprogram instructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to receive participant location datadetermined based on tag derived data and participant role data; receivefield data; compare the participant location data to formation modelsbased at least in part on the participant location data, the participantrole data, and the field data; and determine formation data based on thecomparing the participant location data to the formation models.

In some embodiments, the apparatus may further comprise the at least onememory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus tocompare the participant location data to play models based at least inpart on the participant role data, the field data, and the formationdata; and determine play data based on the comparing the participantlocation data and the formation data to the play models.

In another embodiment, an apparatus is provided for monitoring aparticipant comprising at least one processor and at least one memoryincluding computer program instructions, the at least one memory and thecomputer program instructions configured to, in cooperation with the atleast one processor, cause the apparatus to correlate at least one tagto the participant; correlate at least one sensor to the participant;receive blink data associated with the at least one tag; receive sensorderived data associated with the at least one sensor; determine taglocation data based at least on the blink data; compare the tag locationdata and the sensor derived data to participant dynamics/kineticsmodels; determine participant location data based on the comparing thetag location data and the sensor derived data to the participantdynamics/kinetics models; and determine participant role data based onthe determining participant location data.

In another embodiment, an apparatus is provided for generating play datacomprising at least one processor and at least one memory includingcomputer program instructions, the at least one memory and the computerprogram instructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to receive participant location datadetermined based on tag location data, sensor derived data, andparticipant role data; receive formation data and field data; comparethe participant location data and formation data to play models based atleast in part on the participant role data and the field data; anddetermine play data based on the comparing the participant location dataand the formation data to the play models.

In another embodiment, an apparatus is provided for generating sportingevent analytics comprising at least one processor and at least onememory including computer program instructions, the at least one memoryand the computer program instructions configured to, in cooperation withthe at least one processor, cause the apparatus to receive participantlocation data; receive participant role data; receive formation data;receive play data; determine output events based at least in part on theparticipant location data, the participant role data, the formationdata, and the play data; store the output events in a data store; andprovide at least some of the output events to one or more analyticssystems or control systems.

In another embodiment, a computer program product is provided forgenerating formation data, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to receiveparticipant location data determined based on tag derived data andparticipant role data; receive field data; compare the participantlocation data to formation models based at least in part on theparticipant location data, the participant role data, and the fielddata; and determine formation data based on the comparing theparticipant location data to the formation models.

In some embodiments, the computer program product may further comprisethe computer program instructions at least configured to compare theparticipant location data to play models based at least in part on theparticipant role data, the field data, and the formation data; anddetermine play data based on the comparing the participant location dataand the formation data to the play models.

In another embodiment, a computer program product is provided formonitoring a participant, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to correlate atleast one tag to the participant; correlate at least one sensor to theparticipant; receive blink data associated with the at least one tag;receive sensor derived data associated with the at least one sensor;determine tag location data based at least on the blink data; comparethe tag location data and the sensor derived data to participantdynamics/kinetics models; determine participant location data based onthe comparing the tag location data and the sensor derived data to theparticipant dynamics/kinetics models; and determine participant roledata based on the determining participant location data.

In another embodiment, a computer program product is provided forgenerating play data, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to receiveparticipant location data determined based on tag location data, sensorderived data, and participant role data; receive formation data andfield data; compare the participant location data and formation data toplay models based at least in part on the participant role data and thefield data; and determine play data based on the comparing theparticipant location data and the formation data to the play models.

In another embodiment, a computer program product is provided forgenerating sporting event analytics, the computer program productcomprising a non-transitory computer readable storage medium andcomputer program instructions stored therein, the computer programinstructions comprising program instructions at least configured toreceive participant location data; receive participant role data;receive formation data; receive play data; determine output events basedat least in part on the participant location data, the participant roledata, the formation data, and the play data; store the output events ina data store; and provide at least some of the output events to one ormore analytics systems or control systems.

In one embodiment, a method is provided for evaluating a player that atleast includes correlating at least one tag to the player, receivingblink data transmitted by the at least one tag, and determining taglocation data based on the blink data. The method further includesreceiving player role data, comparing the tag location data to playerdynamics/kinetics models based at least in part on the player role data,determining player location data based on the comparing the tag locationdata to the player dynamics/kinetics models, and determining playerperformance information based on comparing the player location data tostored player location data.

In some embodiments, the stored player location data may be based onprior activity of the player. In some embodiments, the stored playerlocation data may be based on prior activity of another player. In someembodiments, the stored player location data may be based on prioractivity of the player and another player. In some embodiments, thestored player location data may be based on prior activity of the playerand an adversary.

In some embodiments, determining player performance information mayinclude determining a player health profile based, at least in part, oncomparing the player location data to the stored player location data.In some embodiments, the method may further comprise receiving sensorderived data.

In another embodiment, a method is provided for evaluating an officialthat at least includes correlating at least one tag to the official,receiving blink data transmitted by the at least one tag, anddetermining tag location data based on the blink data. The methodfurther includes receiving official role data, comparing the taglocation data to official dynamics/kinetics models based at least inpart on the official role data, determining official location data basedon the comparing the tag location data to the official dynamics/kineticsmodels, and determining official performance information based oncomparing the official location data to stored official location data.

In another embodiment, an apparatus for evaluating a player is providedthat comprises at least one processor and at least one memory includingcomputer program instructions, the at least one memory and the computerprogram instructions configured to, in cooperation with the at least oneprocessor cause the apparatus to correlate at least one tag to theplayer, receive blink data transmitted by the at least one tag, anddetermine tag location data based on the blink data. The apparatus isfurther configured to receive player role data, compare the tag locationdata to player dynamics/kinetics models based at least in part on theplayer role data, determine player location data based on the comparingthe tag location data to the player dynamics/kinetics models, anddetermine player performance information based on comparing the playerlocation data to stored player location data. In some embodiments, theat least one memory and the computer program instructions configured to,in cooperation with the at least one processor, cause the apparatus tofurther receive environmental measurements transmitted by a sensor.

In another embodiment, an apparatus for evaluating an official isprovided that comprises at least one processor and at least one memoryincluding computer program instructions, the at least one memory and thecomputer program instructions configured to, in cooperation with the atleast one processor, cause the apparatus to correlate at least one tagto the official, receive blink data transmitted by the at least one tag,and determine tag location data based on the blink data. The apparatusis further configured to receive official role data, compare the taglocation data to official dynamics/kinetics models based at least inpart on the official role data, determine official location data basedon the comparing the tag location data to the official dynamics/kineticsmodels, and determine official performance information based oncomparing the official location data to stored official location data.

In a further embodiment, a computer program product for evaluating aplayer is provided, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to correlate atleast one tag to the player, receive blink data transmitted by the atleast one tag, and determine tag location data based on the blink data.The computer program instructions further configured to receive playerrole data, compare the tag location data to player dynamics/kineticsmodels based at least in part on the player role data, determine playerlocation data based on the comparing the tag location data to the playerdynamics/kinetics models, and determine player performance informationbased on comparing the player location data to stored player locationdata. The computer program instructions further configured to receiveenvironmental measurements transmitted by a sensor.

In another embodiment, a computer program product for evaluating anofficial is provided, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to correlate atleast one tag to the official, receive blink data transmitted by the atleast one tag, and determine tag location data based on the blink data.The computer program instructions further configured to receive officialrole data, compare the tag location data to official dynamics/kineticsmodels based at least in part on the official role data, determineofficial location data based on the comparing the tag location data tothe official dynamics/kinetics models, and determine officialperformance information based on comparing the official location data tostored official location data.

In a further embodiment, a system for evaluating a player is providedwhich comprises one or more tags and an apparatus comprising at leastone processor and at least one memory including computer programinstructions, the at least one memory and the computer programinstructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to correlate at least one tag to theplayer, receive blink data transmitted by the at least one tag, anddetermine tag location data based on the blink data. The apparatus isfurther configured to receive player role data, compare the tag locationdata to player dynamics/kinetics models based at least in part on theplayer role data, determine player location data based on the comparingthe tag location data to the player dynamics/kinetics models, anddetermine player performance information based on comparing the playerlocation data to stored player location data. In some embodiments, thesystem may further comprise one or more sensors.

In another embodiment, a system for evaluating an official is providedwhich comprises one or more tags and an apparatus comprising at leastone processor and at least one memory including computer programinstructions, the at least one memory and the computer programinstructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to correlate at least one tag to theofficial, receive blink data transmitted by the at least one tag, anddetermine tag location data based on the blink data. The apparatus isfurther configured to receive official role data, compare the taglocation data to official dynamics/kinetics models based at least inpart on the official role data, determine official location data basedon the comparing the tag location data to the official dynamics/kineticsmodels, and determine official performance information based oncomparing the official location data to stored official location data.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates an exemplary environment using a radio frequencylocating system for providing performance analytics in accordance withsome embodiments of the present invention;

FIGS. 2A-C illustrate some exemplary participants carrying tags andsensors that may provide information to a performance analytics systemin accordance with some embodiments of the present invention;

FIGS. 3A-3E are block diagrams showing the input and output of receiversand sensor receivers in accordance with an example embodiment;

FIG. 4 illustrates an exemplary system for providing performanceanalytics in accordance with some embodiments of the present invention;

FIG. 5 illustrates example participant tracking over time in accordancewith some embodiments of the present invention;

FIGS. 6A-12 provide flowcharts of some exemplary processes that may beused in providing performance analytics in accordance with someembodiments of the present invention; and

FIG. 13 illustrates a block diagram of components that may be includedin devices for performing operations in accordance with some embodimentsof the present invention.

DETAILED DESCRIPTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the inventions are shown. Indeed, the invention may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

Overview

Existing performance analytics of sporting events have drawbacks inproviding accurate data about events and participant actions that occurduring a game. Game day data is often manually collected by individualsdocumenting participant actions and play participation during a game.Performance review of a game often requires individuals to manuallyreview game recordings over a number of hours after a game to compileplayer actions and events during play. This performance review is alsooften limited to statistics and data that can be identified or capturedby the individuals watching or reviewing a game or game film. Inaddition, such review and any analytics data flowing therefrom isprovided freely on a non-exclusive basis as anyone with access to gamefilm can compile similar analytics data.

Embodiments of the present invention are directed to methods, systems,apparatuses, and computer readable storage media for providing real-timecollection of data and analysis of participant performance and playstatistics during a game such as by using radio frequency locatingsystems and radio frequency identification (“RFID”).

Embodiments of the present invention may provide for automaticrecognition of formations, plays, and events through the processing ofreal time data (or near real time data) regarding location, change inlocation, velocity, change in acceleration, orientation, or the like,for participants based on an analysis of relevant models and data asdescribed in detail below. The term “participant” as used herein refersto players, officials, game related objects such as the ball, penaltymarkers, line of scrimmage and yard to gain markers, and any othermovable object proximate a field of play.

In embodiments where participants are players, a group or plurality ofparticipants may be grouped into squads (e.g., offense, defense,kickoff, punt, kick return, punt return, field goal, infield, outfield,bullpen, etc.) and/or teams (e.g., football team, baseball team, swimteam, etc.). Participants on the same team are called team mates;participants on different teams are called adversaries.

Embodiments of the present invention may provide for automated datacollection with reduced errors, as well as providing additionalstatistics that may not be available with current systems. Additionally,embodiments may provide for rapid (i.e., near instantaneous) productionof game review documentation (e.g., playbooks). Embodiments of thepresent invention may also provide additional and exclusive data andanalysis that may be securely licensed without concern that similaranalytics may be readily reproduced without a system configured as setforth below.

Embodiments of the present invention may allow for the simultaneoustracking of a plurality of participants and may provide for indicationsof player statistics and/or potential play events in real time (or nearreal time). Such indications may be output to a variety of systemsincluding, without limitation, a visualization system (e.g., an enhancedtelevision broadcast system or computer graphics visualization system),a game operations system, a camera control system, a team analyticssystem, a league analytics system, and a statistics system.

Embodiments of the present invention are illustrated in the appendedfigures and description below in relation to the sport of Americanfootball. However, as will be apparent to one of ordinary skill in theart in view of this disclosure, the inventive concepts herein describedare not limited to football and may be applied to various otherapplications including, without limitation, other sports or group eventssuch as baseball, basketball, golf, hockey, soccer, racing ormotorsports, competitive events, and the like.

Example RF Locating System Architecture

FIG. 1 illustrates a radio frequency locating system useful fordetermining the location of an object (e.g. a football player on afootball field) by determining RF location tag 102 (e.g., a ultra-wideband (UWB) location tag) location information at each receiver 106(e.g., UWB reader, etc.); a timing reference clock to synchronize thefrequency of counters within each receiver 106; and, in some examples, areference tag 104, preferably a UWB transmitter, positioned at knowncoordinates to enable phase offset between counters to be determined.The systems described herein may be referred to as either“multilateration” or “geolocation” systems; terms which refer to theprocess of locating a signal source by solving for the mathematicalintersection of multiple hyperbolae determined by the difference ofarrival times of a signal received at multiple receivers.

In some examples, the system comprising at least the tags 102 and thereceivers 106 is configured to provide two dimensional and/or threedimensional precision localization (e.g., subfoot resolutions), even inthe presence of multipath interference, due in part to the use of shortnanosecond duration pulses whose time-of-flight can be accuratelydetermined using detection circuitry, such as in the receivers 106,which can trigger on the leading edge of a received waveform. In someexamples, this short pulse characteristic allows necessary data to beconveyed by the system at a higher peak power, but lower overall powerlevels, than a wireless system configured for high data ratecommunications, yet still operate within local regulatory requirementswhich may limit overall power levels.

In some examples, the tags 102 may operate with an instantaneous −3 dBbandwidth of approximately 400 MHz and an average transmission ratebelow a 187.5 kHz regulatory cutoff. In such examples, the predictedmaximum range of the system, operating at 6.0 GHz, is roughly 311meters. Such a configuration advantageously satisfies constraintsapplied by regulatory bodies related to peak and average power densities(e.g., effective isotropic radiated power density), while stilloptimizing system performance related to range and interference. Infurther examples, tag transmissions with a −3 dB bandwidth ofapproximately 400 MHz yields, in some examples, an instantaneouspulsewidth of roughly 2.5 nanoseconds which enables a resolution tobetter than 30 centimeters.

Referring again to FIG. 1, the object to be located has an attached RFlocation tag 102, preferably a tag having a UWB transmitter, thattransmits a signal comprising a burst (e.g., 72 pulses at a burst rateof 1 Mb/s), and optionally, a burst having a tag data packet that mayinclude tag data elements that may include, but are not limited to, atag unique identification number (tag UID), other identificationinformation, a sequential burst count, stored tag data, or other desiredinformation for object or personnel identification, inventory control,etc. In some embodiments, the tag data packet may include atag-individual correlator that can be used to associate a specificindividual (e.g., participant) with a specific tag. In some examples,the sequential burst count (e.g., a packet sequence number) from eachtag 102 may be advantageously provided in order to permit, at a receiverhub 108, correlation of time of arrival (TOA) measurement data fromvarious receivers 106.

In some examples, the RF location tag 102 may employ UWB waveforms(e.g., low data rate waveforms) to achieve extremely fine resolutionbecause of their extremely short pulse (i.e., sub-nanosecond tonanosecond, such as a 2 ns (1 ns up and 1 ns down)) durations. As such,the tag data packet may be of a short length (e.g., 72-112 bits in someexample embodiments), that advantageously enables a higher throughputand higher transmission rates. In some examples, higher throughputand/or higher transmission rates may result in larger datasets forfiltering to achieve a more accurate location estimate. In someexamples, rates of up to approximately 2600 updates per second can beaccommodated without exceeding regulatory requirements. Alternatively oradditionally, in some examples, the length of the tag data packets, inconjunction with other system functionality, may also result in a longerbattery life (e.g., a 3.0 v 1 A-hr lithium cell battery may result in atag battery life in excess of 3.8 years).

In some examples, one or more other tags, such as a reference tag 104,may be positioned within and/or about a monitored area or zone, such asmonitored area 100 illustrated herein as a football field. In someexamples, the reference tag 104 may be configured to transmit a signalthat is used to measure the relative phase (e.g., the count offree-running counters) of non-resettable counters within the receivers106.

One or more (preferably four or more) receivers 106 are also atlocations with predetermined coordinates within and/or around themonitored area 100. In some examples, the receivers 106 may be connectedin a “daisy chain” fashion to advantageously allow for a large number ofreceivers 106 to be interconnected over a significant monitored area inorder to reduce and simplify cabling, reduce latency, provide powerand/or the like. Each of the receivers 106 includes a receiver forreceiving transmissions, such as UWB transmissions, and preferably, apacket decoding circuit that extracts a time of arrival (TOA) timingpulse train, transmitter ID, packet number and/or other information thatmay have been encoded in the tag transmission signal (e.g., materialdescription, personal information, etc.) and is configured to sensesignals transmitted by the tags 102 and one or more reference tags 104(if present).

Each receiver 106 includes a time measuring circuit that measures timedifferences of arrival (TDOA) of tag bursts. The time measuring circuitis phase-locked (e.g., phase differences do not change and thereforerespective frequencies are identical) with a common digital referenceclock signal distributed via cable connection from a receiver hub 108having a central timing reference clock generator. The reference clocksignal establishes a common timing reference for the receivers 106.Thus, multiple time measuring circuits of the respective receivers 106are synchronized in frequency, but not necessarily in phase. While theretypically may be a phase offset between any given pair of receivers inthe receivers 106, the offset is readily determined through use of areference tag 104. Alternatively or additionally, each receiver may besynchronized wirelessly via virtual synchronization without a dedicatedphysical timing channel.

In some example embodiments, the receivers 106 are configured todetermine various attributes of the received signal. Since measurementsare determined at each receiver 106, in a digital format, rather thananalog, signals are transmittable to the receiver hub 108.Advantageously, because packet data and measurement results can betransferred at high speeds to a receiver memory, the receivers 106 canreceive and process tag (and corresponding object) locating signals on anearly continuous basis. As such, in some examples, the receiver memoryallows for a high burst rate of tag events (i.e., tag data packets) tobe captured.

Data cables or wireless transmissions may convey measurement data fromthe receivers 106 to the receiver hub 108 (e.g., the data cables mayenable a transfer speed of 2 Mbps). In some examples, measurement datais transferred to the receiver hub at regular polling intervals.

As such, the receiver hub 108 determines or computes tag location (i.e.,object location) by processing TDOA measurements related to multipledata packets detected by the receivers 106. In some example embodiments,the receiver hub 108 may be configured to resolve the coordinates of atag using nonlinear optimization techniques. The receiver hub 108 mayalso be referred to herein as a locate engine or a receiver hub/locateengine.

In some examples, the system described herein may be referred to as an“over-specified” or “over-determined” system. As such, the receiver hub108 may then calculate one or more valid (i.e., most likely) locationsbased on a set of measurements and/or one or more incorrect (i.e., lesslikely) locations. For example, a location may be calculated that isimpossible due the laws of physics (e.g., a tag on a football playerthat travels more than 100 yards in 1 second) or may be an outlier whencompared to other locations. As such one or more algorithms orheuristics may be applied to minimize such error.

One such algorithm for error minimization, which may be referred to as atime error minimization algorithm, may be described as:

$ɛ = {\sum\limits_{j = 1}^{N}{\sum\limits_{k = {j + 1}}^{N} \left\{ {\left( {t_{j} - t_{k}} \right) - {\frac{1}{c}\left\lbrack {\left\lbrack {\left( {x - x_{j}} \right)^{2} + \left( {y - y_{j}} \right)^{2} + \left( {z - z_{j}} \right)^{2}} \right\rbrack^{\frac{1}{2}} - \left. \quad\left. \quad\left\lbrack {\left( {x - x_{k}} \right)^{2} + \left( {y - y_{k}} \right)^{2} + \left( {z - z_{k}} \right)^{2}} \right\rbrack^{\frac{1}{2}} \right\rbrack \right\}^{2}} \right.}} \right.}}$

where N is the number of receivers, c is the speed of light, x_(j,k),y_(j,k) and z_(j,k) are the coordinates of the receivers and t_(j,k) arethe arrival times received at each of the receivers. Note that only timedifferences may be evaluated at hub 108 in some example embodiments. Thestarting point for the minimization may be obtained by first doing anarea search on a coarse grid of x, y and z over an area defined by theuser and followed by a localized steepest descent search.

Another or second algorithm for error minimization, which may bereferred to as a distance error minimization algorithm, may be definedby:

$ɛ = {\sum\limits_{j = 1}^{N}\left\lbrack {\left\lbrack {\left( {x - x_{j}} \right)^{2} + \left( {y - y_{j}} \right)^{2} + \left( {z - z_{j}} \right)^{2}} \right\rbrack^{\frac{1}{2}} - {c\left( {t_{j} - t_{0}} \right)}} \right\rbrack^{2}}$

where time and location differences are replaced by theirnon-differential values by incorporating an additional unknown dummyvariable, t₀, which represents an absolute time epoch. The startingpoint for this algorithm is fixed at the geometric mean position of allactive receivers. No initial area search is needed, and optimizationproceeds through the use of a DavidonFletcher-Powell (DFP) quasi-Newtonalgorithm in some examples. In other examples, a steepest descentalgorithm may be used.

In order to determine the coordinates of a tag (T), in some examples andfor calibration purposes, a reference tag (e.g., reference tag 104) ispositioned at a known coordinate position (x_(T), y_(T), z_(T)).

In further example embodiments, a number N of receivers {R_(j): j=1, . .. , N} (e.g., receivers 106) are positioned at known coordinates (x_(R)_(j) , y_(R) _(j) , z_(R) _(j) ), which are respectively located atdistances, such as:

d _(Rj)=√{square root over ((x _(Rj) −x _(T))²+(y _(Rj) −y _(T))²+(z_(Rj) −z _(T))²)}{square root over ((x _(Rj) −x _(T))²+(y _(Rj) −y_(T))²+(z _(Rj) −z _(T))²)}{square root over ((x _(Rj) −x _(T))²+(y_(Rj) −y _(T))²+(z _(Rj) −z _(T))²)}

from a reference tag.

Each receiver R_(j) utilizes, for example, a synchronous clock signalderived from a common frequency time base, such as clock generator.Because the receivers are not synchronously reset, an unknown, butconstant offset O_(j) exists for each receiver's internal free runningcounter. The value of the offset O_(j) is measured in terms of thenumber of fine resolution count increments (e.g., a number ofnanoseconds for a one nanosecond resolution system).

The reference tag is used to calibrate the radio frequency locatingsystem as follows:

The reference tag emits a signal burst at an unknown time τ_(R). Uponreceiving the signal burst from the reference tag, a count N_(R) _(j) asmeasured at receiver R_(j) is given by

N _(R) _(j) =βτ_(R) +O _(j) +βd _(R) _(j) /c

where c is the speed of light and β is the number of fine resolutioncount increments per unit time (e.g., one per nanosecond). Similarly,each object tag T_(i) of each object to be located transmits a signal atan unknown time τ_(i) to produce a count

N _(i) _(j) =βτ_(i) +O _(j) +βd _(i) _(j) /c

at receiver R_(j) where d_(i) _(j) is the distance between the objecttag T_(i) and the receiver at receiver R_(j). Note that τ_(i) isunknown, but has the same constant value for receivers of all receiversR_(j). Based on the equalities expressed above for receivers R_(j) andR_(k) and given the reference tag information, differential offsetsexpressed as differential count values are determined as follows:

${N_{R_{j}} - N_{R_{k}}} = {\left( {O_{j} - O_{k}} \right) + {\beta \left( {\frac{d_{R_{j}}}{c} - \frac{d_{R_{k}}}{c}} \right)}}$${{or}\left( {O_{j} - O_{k}} \right)} = {{\left( {N_{R_{j}} - N_{R_{k}}} \right) - {\beta \left( {\frac{d_{R_{j}}}{c} - \frac{d_{R_{k}}}{c}} \right)}} = \Delta_{j_{k}}}$

Δ_(jk) is constant as long as d_(Rj)−d_(Rk) remains constant, (whichmeans the receivers and tag are fixed and there is no multipathsituation) and β is the same for each receiver. Note that Δ_(j) _(k) isa known quantity, since N_(R) _(j) , N_(R) _(k) , β, d_(R) _(j) /c, andd_(R) _(k) /c are known. That is, the differential offsets betweenreceivers R_(j) and R_(k) may be readily determined based on thereference tag transmissions. Thus, again from the above equations, foran object tag (T_(i)) transmission arriving at receivers R_(j) andR_(k):

N _(i) _(j) −N _(i) _(k) =(O _(j) −O _(k))+β(d _(i) _(j) /c−d _(i) _(k)/c)=Δ_(j) _(k) +β(d _(i) _(j) /c−d _(i) _(k) /c)

or,

d _(i) _(j) −d _(i) _(k) =(c/β)[N _(i) _(j) −N _(i) k−Δ _(j) _(k) ],

The process further includes determining a minimum error value E_(i),for each object tag T_(i), according to the functional relationship:

$E_{i} = {\min\limits_{({x,y,z})}{\sum\limits_{j}{\sum\limits_{k > j}\left\lbrack {\left( {d_{i_{j}} - d_{i_{k}}} \right) - \left( {{{dist}\left( {T_{x,y,z},R_{j}} \right)} - {{dist}\left( {T_{x,y,z},R_{k}} \right)}} \right)} \right\rbrack^{2}}}}$

-   -   where dist (T_(x,y,x) ,R _(j))=√{square root over ((x_(R) _(j)        −x)²+(y_(R) _(j) −y)²+(z _(R) _(j) −z)²)}{square root over        ((x_(R) _(j) −x)²+(y_(R) _(j) −y)²+(z _(R) _(j) −z)²)}{square        root over ((x_(R) _(j) −x)²+(y_(R) _(j) −y)²+(z _(R) _(j) −z)²)}

is the Euclidean distance between point (x,y,z) and the coordinates ofthe j^(th) receiver R_(j). The minimization solution (x′,y′,z′) is theestimated coordinate location for the i^(th) tag at t₀.

In an example algorithm, this proceeds according to:

$ɛ = {\sum\limits_{j = 1}^{N}\left\lbrack {\left\lbrack {\left( {x - x_{j}} \right)^{2} + \left( {y - y_{j}} \right)^{2} + \left( {z - z_{j}} \right)^{2}} \right\rbrack^{\frac{1}{2}} - {c\left( {t_{j} - t_{0}} \right)}} \right\rbrack^{2}}$

where each arrival time, t_(j), is referenced to a particular receiver(receiver “1”) as follows:

$t_{j} = {\frac{1}{\beta}\left( {N_{j} - N_{1} - \Delta_{j_{k}}} \right)}$

and the minimization is performed over variables (x, y, z, t₀) to reacha solution (x′, y′, z′, t₀′).

In some example embodiments, the location of a tag 102 may then beoutput to a receiver processing and distribution system 110 for furtherprocessing of the location data to advantageously providevisualizations, predictive analytics, statistics and/or the like.

The exemplary radio frequency locating system of FIG. 1 may be used inproviding performance analytics in accordance with some embodiments ofthe present invention. In the environment of FIG. 1, data may becaptured and analyzed, such as during a sporting event to identifyevents, statistics, and other data useful to a sports team, league,viewer, licensee, or the like. In some embodiments, data associated witha number of objects or participants (e.g., players, officials, balls,game equipment, etc.) on a playing field, such as monitored area 100,may be generated and provided to a performance analytics system. Assuch, as further discussed in connection with FIGS. 2A-C below, eachobject may have one or more attached tags 102 (such as to equipment wornby a player) to be used to track data such as location, change oflocation, speed, or the like of each object. In some embodiments,additional sensors, such as, without limitation, accelerometers,magnetometers, time-of-flight sensors, health sensors, temperaturesensors, moisture sensors, light sensors, or the like, may be attachedto each object to provide further data to the performance analyticssystem. Such additional sensors may provide data to the tag 102, eitherthrough a wired or wireless connection, to be transmitted to thereceivers 106 or the sensors may be configured to transmit data toreceivers (i.e., sensor receivers) separately from tags 102.

One or more of the receivers 106 may receive transmissions from tags 102and transmit the blink data to a receiver hub 108. The receiver hub 108may process the received data to determine tag location for the tags102. The receiver hub 108 may transmit the tag location data to one ormore processors, such as receiver processing and distribution system110. Receiver processing and distribution system 110 may use one or moremodules (e.g., processing engines) and one or more databases to identifythe object each of the tags 102 is associated with, such as a player,official, ball, or the like.

In some embodiments, multiple tags 102 (as well as other sensors) may beattached to the equipment worn by an individual player, official, orother participant. The receiver processing and distribution system 110may use one or more databases to associate the tag identifier (e.g., atag UID) of each tag 102 with each player, official, object, or otherparticipant and correlate the tag location data and/or other tag andsensor derived data for multiple tags 102 that are associated with aparticular player, official, object, or other participant.

As discussed in greater detail below, the receiver processing anddistribution system 110 may then use the tag location data and/or othertag and sensor derived data to determine player dynamics, such as aplayer's location, how the location is changing with time, orientation,velocity, acceleration, deceleration, total yardage, or the like. Thereceiver processing and distribution system 110 may also use the taglocation data and/or other tag and sensor derived data to determinedynamics for other participants such as the officials, the ball, penaltymarkers, line of scrimmage or yards to gain markers, or the like, foruse in generating data for performance analytics. The receiverprocessing and distribution system 110 may also use the data and one ormore databases to determine team formations, play activity, events,statistics, or the like, such as by comparing the data to various modelsto determine the most likely formation or play or the events that haveoccurred during a game. The receiver processing and distribution system110 may also use the data to provide statistics or other output data forthe players, teams, and the game.

As will be apparent to one of ordinary skill in the art, the inventiveconcepts herein described are not limited to use with the UWB based RFlocating system shown in FIG. 1. Rather, in various embodiments, theinventive concepts herein described may be applied to various otherlocating systems especially those that are configured to provide robustlocation resolution (i.e., subfoot location resolution).

Example Tag/Sensor Positioning and Participant Correlation

FIG. 1 shows a monitored area 100. The monitored area 100 comprises aplurality of positions at one or more time epochs. The plurality ofpositions may be divided into one or more regions, called zones. Eachzone may be described by one or more coordinate systems, such as a localNED (North-East-Down) system, a latitude-longitude system, or even ayard line system as might be used for an American football game. Alocation is a description of a position, or a plurality of positions,within the monitored area. For example, a field marker at theintersection of the south goal line and west out of bounds line at Bankof America Stadium in Charlotte, N.C. could be described as {0,0,0} in alocal NED system, or 35.225336 N 80.85273 W longitude 751 ft. altitudeon a latitude-longitude system, or simply “Panthers Goal Line” in a yardline system. Because different types of locating systems or differentzones within a single locating system may use different coordinatesystems, a Geographical Information System or similar monitored areadatabase may be used to associate location data. One type ofGeographical Information System describing at least a field of play maybe called Field Data.

FIGS. 2A-C illustrate some exemplary participants that may provideinformation to a performance analytics system in accordance with someembodiments of the present invention. FIG. 2A illustrates a player 202(e.g., a football player) wearing equipment having attached tags 102 inaccordance with some embodiments. In particular, the depicted player 202is wearing shoulder pads having tags 102 affixed to opposite sidesthereof. This positioning advantageously provides an elevated broadcastposition for each tag 102 thereby increasing its communicationeffectiveness.

Additional sensors 203 may be attached to equipment worn by player 202,such as accelerometers, magnetometers, time-of-flight sensors, healthmonitoring sensors (e.g., blood pressure sensors, heart monitors,respiration sensors, moisture sensors, temperature sensors), lightsensors, or the like. The additional sensors 203 may be affixed toshoulder pads, the helmet, the shoes, rib pads, elbow pads, the jersey,the pants, a bodysuit undergarment, gloves, arm bands, wristbands, andthe like.

Sensors 203 may be configured to communicate with receivers (e.g.,receivers 106 of FIG. 1) directly or indirectly through tags 102 orother transmitters. For example, in one embodiment, a sensor 203 may beconnected, wired (e.g., perhaps through wires sewn into a jersey orbodysuit undergarment) or wirelessly, to tags 102 to provide sensor datato tags 102, which is then transmitted to the receivers 106. In anotherembodiment, a plurality of sensors (not shown) may be connected to adedicated antenna or transmitter, perhaps positioned in the helmet,which may transmit sensor data to one or more receivers.

FIG. 2B illustrates a game official 206 wearing equipment havingattached tags 102 and sensors 203 in accordance with some embodiments.In the depicted embodiment, tags 102 are attached to the official'sjersey proximate opposite shoulders. Sensors 203 are positioned inwristbands worn on the official's wrists as shown. Sensors 203 may beconfigured to communicate with receivers (e.g., receivers 106 of FIG. 1)directly or indirectly through tags 102 or other transmitters asdiscussed above in connection with FIG. 2A.

As discussed in greater detail below, the positioning of sensors 203(here, accelerometers) proximate the wrists of the official may allowthe receiver processing and distribution system 110 to determineparticular motions, movements, or activities of the official 206 for usein determining events (e.g., winding of the game clock, first down,touchdown, or the like). The official 206 may also carry otherequipment, such as penalty flag 208, which may also have a tag 102 (andoptionally one or more sensors) attached to provide additional data tothe receiver processing and distribution system 110. For example, thereceiver processing and distribution system 110 may use tag locationdata from the penalty flag 208 to determine when the official is merelycarrying the penalty flag 208 versus when the official is using thepenalty flag 208 to indicate an event, such as a penalty (e.g., bythrowing the penalty flag 208).

FIG. 2C illustrates an example of a ball 210 having tags 102 attached orembedded in accordance with some embodiments. Additionally, sensors 203may be attached to or embedded in the ball 210, such as accelerometers,time-of-flight sensors, or the like. In some embodiments, the sensor 203may be connected, wired or wirelessly, to tag 102 to provide sensor datato tag 102 which is then transmitted to the receivers 106. In someembodiments, the sensor 203 may transmit sensor data to receiversseparately from the tag 102, such as described above in connection withFIG. 2A.

As will be apparent to one of ordinary skill in the art in view of thisdisclosure, once the tags 102 and sensors 203 of FIGS. 2A-C arepositioned on participants, they may be correlated to such participants.For example, in some embodiments, unique tag or sensor identifiers(“unique IDs”) may be correlated to a participant profile (e.g., JohnSmith—running back, Fred Johnson—line judge official, or ID 027—one ofseveral game balls, etc.) and stored to a remote database accessible tothe performance analytics system as discussed in greater detail below.Each participant profile may further include or be correlated with avariety of data including, but not limited to, biometric data (e.g.,height, weight, health data, etc.), role data, team ID, performancestatistics, and other data that may be apparent to one of skill in theart in view of the foregoing description.

In some embodiments, such participant profile or role data may bepre-defined and stored in association with the unique tag or sensoridentifiers. In other embodiments, the participant profile or role datamay also be “learned” by the system as a result of received tag orsensor data, formation data, play data, event data, and/or the like. Forexample, in some embodiments the system may determine that a tag orsensor is not correlated to a participant profile and may analyze datareceived from the tag and/or sensor to determine possible participantroles, etc., which may be ranked and then selected/confirmed by thesystem or by a user after being displayed by the system. In someembodiments, the system may determine possible participant roles (i.e.,participant role data) based on determined participant location data(e.g., movement patterns, alignment position, etc.).

In some embodiments, as described in greater detail below, theparticipant profile or role data may also be updated by the system(i.e., to produce a data set for the participant that is far more robustthan that established at initial registration) as a result of receivedtag or sensor data, formation data, play data, event data, and/or thelike. In some embodiments, the participant profile and/or role data maybe used in a performance analytics system to weight the actions of theparticipants during analysis to assist in qualifying what is occurring,such as in determining formations, plays, events, etc.

Tag ID and Sensor Data Transmission Architecture

FIGS. 3A, 3B, 3C, 3D, and 3E show block diagrams of various differentarchitectures that may be utilized in transmitting signals from one ormore tags and sensors to one or more receivers of a receiver processingand analytics system in accordance with embodiments of the invention. Insome embodiments, the depicted architectures may be used in connectionwith the receiver processing and analytics system 110 of FIG. 1. Morethan one of these architectures may be used together in a single system.

FIG. 3A shows a RF location tag 102, such as that shown in FIG. 1, whichmay be configured to transmit a tag signal to one or more receivers 106.The one or more receivers 106 may transmit a receiver signal to thereceiver hub/locate engine 108.

The depicted RF location tag 102 may generate or store a tag uniqueidentifier (“tag UID”) and/or tag data as shown. The tag data mayinclude useful information such as the installed firmware version, lasttag maintenance date, configuration information, and/or a tag-individualcorrelator. The tag-individual correlator may comprise data thatindicates that a monitored individual (e.g., participant) is associatedwith the RF location tag 102 (e.g. name, uniform number and team,biometric data, tag position on individual, i.e., right wrist). As willbe apparent to one of skill in the art in view of this disclosure, thetag-individual correlator may be stored to the RF location tag 102 whenthe tag is registered or otherwise associated with an individual. Whileshown as a separate field for illustration purposes, one of ordinaryskill in the art may readily appreciate that the tag-individualcorrelator may be part of any tag data or even omitted from the tag.

The tag signal transmitted from RF location tag 102 to receiver 106 mayinclude “blink data” as it is transmitted at selected intervals. This“blink rate” may be set by the tag designer or the system designer tomeet application requirements. In some embodiments it is consistent forone or all tags; in some embodiments it may be data dependent. Blinkdata includes characteristics of the tag signal that allow the tagsignal to be recognized by the receiver 106 so the location of the RFlocation tag 102 may be determined by the locating system. Blink datamay also comprise one or more tag data packets. Such tag data packetsmay include any data from the tag 102 that is intended for transmissionsuch as, for example in the depicted embodiment, a tag UID, tag data,and a tag-individual correlator. In the case of TDOA systems, the blinkdata may be or include a specific pattern, code, or trigger that thereceiver 106 (or downstream receiver processing and analytics system)detects to identify that the transmission is from a RF location tag 102(e.g., a UWB tag).

The depicted receiver 106 receives the tag signal, which includes blinkdata and tag data packets as discussed above. In one embodiment, thereceiver 106 may pass the received tag signal directly to the receivehub/locate engine 108 as part of its receiver signal. In anotherembodiment, the receiver 106 could perform some basic processing on thereceived tag signal. For instance, the receiver could extract blink datafrom the tag signal and transmit the blink data to the receivehub/locate engine 108. The receiver could transmit a time measurement tothe receive hub/locate engine 108 such as a TOA measurement and/or aTDOA measurement. The time measurement could be based on a clock timegenerated or calculated in the receiver, it could be based on a receiveroffset value as explained at paragraph [0138] above, it could be basedon a system time, and/or it could be based on the time difference ofarrival between the tag signal of the RF location tag 102 and the tagsignal of a RF reference tag (e.g., tag 104 of FIG. 1). The receiver 106could additionally or alternatively determine a signal measurement fromthe tag signal (such as a received signal strength indication (RSSI), adirection of signal, signal polarity, or signal phase) and transmit thesignal measurement to the receive hub/locate engine 108.

FIG. 3B shows a RF location tag 202 and sensor 203, such as those wornon an individual's person as shown in FIG. 2, which may be configured totransmit tag signals and sensor signals, respectively, to one or morereceivers 106, 166. The one or more receivers 106, 166 may then transmitreceiver signals to the receiver hub/locate engine 108. One or morereceivers 106, 166 may share physical components, such as a housing orantenna.

The depicted RF location tag 202 may comprise a tag UID and tag data(such as a tag-individual correlator) and transmit a tag signalcomprising blink data as discussed in connection with FIG. 3A above. Thedepicted sensor 203 may generate and/or store a sensor UID, additionalstored sensor data (e.g. a sensor-individual correlator, sensor type,sensor firmware version, last maintenance date, the units in whichenvironmental measurements are transmitted, etc.), and environmentalmeasurements. The “additional stored sensor data” of the sensor 203 mayinclude any data that is intended for transmission, including but notlimited to a RF location tag 202, a reference tag (e.g., 104 of FIG. 1),a sensor receiver, a receiver 106, and/or the receiver/hub locate engine108.

The sensor-individual correlator may comprise data that indicates that amonitored individual is associated with the sensor 203 (e.g., name,uniform number and team, biometric data, sensor position on individual,i.e., right wrist). As will be apparent to one of skill in the art inview of this disclosure, the sensor-individual correlator may be storedto the sensor 203 when the sensor is registered or otherwise associatedwith an individual. While shown as a separate field for illustrationpurposes, one of ordinary skill in the art may readily appreciate thatthe sensor-individual correlator may be part of any additional storedsensor data or omitted from the sensor altogether.

Sensors such as sensor 203 that are structured according to embodimentsof the invention may sense or determine one or more environmentalconditions (e.g. temperature, pressure, pulse, heartbeat, rotation,velocity, acceleration, radiation, position, chemical concentration,voltage) and store or transmit “environmental measurements” that areindicative of such conditions. To clarify, the term “environmentalmeasurements” includes measurements concerning the environment proximatethe sensor including, without limitation, ambient information (e.g.,temperature, position, humidity, etc.) and information concerning anindividual's health, fitness, operation, and/or performance.Environmental measurements may be stored or transmitted in either analogor digital form and may be transmitted as individual measurements, as aset of individual measurements, and/or as summary statistics. Forexample, temperature in degrees Celsius may be transmitted as {31}, oras {33, 32, 27, 22, 20, 23, 27, 30, 34, 31}, or as {27.9}. In someembodiments, the sensor-individual correlator could be determined atleast in part from the environmental measurements.

In the depicted embodiment, RF location tag 202 transmits a tag signalto receiver 106 and sensor 203 transmits a sensor signal to sensorreceiver 166. The sensor signal may comprise one or more sensorinformation packets. Such sensor information packets may include anydata or information from the sensor 203 that is intended fortransmission such as, for example in the depicted embodiment, sensorUID, additional stored sensor data, sensor-individual correlator, andenvironmental measurements. A receiver signal from receiver 106 and asensor receiver signal from sensor receiver 166 may be transmitted viawired or wireless communication to receiver hub/locate engine 108 asshown.

FIG. 3C depicts a sensor 203 communicating through a RF location tag 202in accordance with various embodiments. In one embodiment, the sensor203 may be part of (i.e., reside in the same housing or assemblystructure) of the RF location tag 202. In another embodiment, the sensor203 may be distinct from (i.e., not resident in the same housing orassembly structure) the RF location tag 202 but configured tocommunicate wirelessly or via wired communication with the RF locationtag 202.

In one embodiment, the RF location tag 202, the sensor 203, or both, maygenerate and/or store a tag-sensor correlator that indicates anassociation between a RF location tag 202 and a sensor 203 (e.g., tagUID/sensor UID, distance from tag to sensor in a particular stance, setof sensors associated with a set of tags, sensor types associated with atag, etc.). In the depicted embodiment, both the RF location tag 202 andthe sensor 203 store the tag-sensor correlator.

In the depicted embodiment, sensor 203 transmits a sensor signal to RFlocation tag 202. The sensor signal may comprise one or more sensorinformation packets as discussed above. The sensor information packetsmay comprise the sensor UID, a sensor-individual correlator, additionalstored sensor data, the tag-sensor correlator, and/or the environmentalmeasurements. The RF location tag 202 may store some portion of, or allof, the sensor information packets locally and may package the sensorinformation packets into one or more tag data packets for transmissionto receiver 106 as part of a tag signal or simply pass them along aspart of its tag signal.

FIG. 3D illustrates an example communication structure for a referencetag 104 (e.g., reference tag 104 of FIG. 1), an RF location tag 202, asensor 203, and two receivers 106 in accordance with one embodiment. Thedepicted reference tag 104 is a RF location tag and thus may include tagdata, a tag UID, and is capable of transmitting tag data packets. Insome embodiments, the reference tag 104 may form part of a sensor andmay thus be capable of transmitting sensor information packets.

The depicted sensor 203 transmits a sensor signal to RF reference tag104. The RF reference tag 104 may store some portion or some or all ofthe sensor information packets locally and may package the sensorinformation packets into one or more tag data packets for transmissionto receiver 106 as part of a tag signal, or simply pass them along aspart of its tag signal.

As was described above in connection with FIG. 1, the receivers 106 ofFIG. 3D are configured to receive tag signals from the RF location tag202 and the reference tag 104. Each of these tag signals may includeblink data, which may comprise tag UIDs, tag data packets, and/or sensorinformation packets. The receivers 106 each transmit receiver signalsvia wired or wireless communication to the receiver hub/locate engine108 as shown.

FIG. 3E illustrates an example communication structure between an RFlocation tag 202, a plurality of receivers 106, and a variety of sensortypes including, without limitation, a sensor 203, a diagnostic device233, a triangulation positioner 243, a proximity positioner 253, and aproximity label 263 in accordance with various embodiments. In thedepicted embodiment, none of the sensors 203, 233, 243, 253 form part ofan RF location tag 202 or reference tag 104. However, each may comprisea sensor UID and additional stored sensor data. Each of the depictedsensors 203, 233, 243, 253 transmits sensor signals comprising sensorinformation packets.

In the depicted embodiment, receiver 106 is configured to receive a tagsignal from RF location tag 202 and a sensor signal directly from sensor203. In such embodiments, sensor 203 may be configured to communicate ina communication protocol that is common to RF location tag 202 as willbe apparent to one of ordinary skill in the art in view of thisdisclosure.

FIG. 3E depicts one type of sensor referred to herein as a “proximityinterrogator”. The proximity interrogator 223 can include circuitryoperative to generate a magnetic, electromagnetic, or other field thatis detectable by a RF location tag 202. While not shown in FIG. 3E, aproximity interrogator 223 may include a sensor UID and other tag andsensor derived data or information as discussed above.

In some embodiments, the proximity interrogator 223 is operative as aproximity communication device that can trigger a RF location tag 202(e.g., when the RF location tag 202 detects the field produced by theproximity interrogator 223) to transmit blink data under an alternateblink pattern or blink rate. The RF location tag can initiate apreprogrammed (and typically faster) blink rate to allow more locationpoints for tracking an individual. In some embodiments, the RF locationtag may not transmit a tag signal until triggered by the proximityinterrogator 223. In some embodiments the RF location tag 202 may betriggered when the RF location tag 202 moves near (e.g., withincommunication proximity to) a proximity interrogator 223. In someembodiments, the RF location tag may be triggered when the proximityinterrogator 223 moves near to the RF location tag 202.

In other embodiments, the RF location tag 202 may be triggered when abutton is pressed or a switch is activated on the proximity interrogator223 or on the RF location tag itself. For example, a proximityinterrogator 223 could be placed at the start line of a racetrack. Everytime a car passes the start line, a car-mounted RF location tag 202senses the signal from the proximity interrogator and is triggered totransmit a tag signal indicating that a lap has been completed. Asanother example, a proximity interrogator 223 could be placed at aGatorade cooler. Each time a player or other participant fills a cupfrom the cooler a participant-mounted RF location tag 202 senses thesignal from the proximity interrogator and is triggered to transmit atag signal indicating that Gatorade has been consumed. As anotherexample, a proximity interrogator 223 could be placed on a medical cart.When paramedics use the medical cart to pick up a participant (e.g., aplayer) and move him/her to the locker room, a participant-mounted RFlocation tag 202 senses the signal from the proximity interrogator andis triggered to transmit a tag signal indicating that they have beenremoved from the game. As explained, any of these post-triggered tagsignals may differ from pre-triggered tag signals in terms of any aspectof the analog and/or digital attributes of the transmitted tag signal.

FIG. 3E depicts another type of sensor that is generally not worn by anindividual but is referred to herein as a “diagnostic device”. However,like other sensors, diagnostic devices may measure one or moreenvironmental conditions and store corresponding environmentalmeasurements in analog or digital form.

While the depicted diagnostic device 233 is not worn by an individual,it may generate and store a sensor-individual correlator for associationwith environmental measurements taken in connection with a specificindividual. For example, in one embodiment, the diagnostic device 233may be a blood pressure meter that is configured to store asenvironmental measurements blood pressure data for various individuals.Each set of environmental measurements (e.g., blood pressure data) maybe stored and associated with a sensor-individual correlator.

The depicted diagnostic device 233 is configured to transmit a sensorsignal comprising sensor information packets to a sensor receiver 166.The sensor information packets may comprise one or more of the sensorUID, the additional stored data, the environmental measurements, and/orthe sensor-individual correlator as discussed above. The sensor receiver166 may associate some or all of the data from the sensor informationpackets with other stored data in the sensor receiver 166 or with datastored or received from other sensors, diagnostic devices, RF locationtags 102, or reference tags. The sensor receiver 166 transmits a sensorreceiver signal to a receiver hub/locate engine 108.

Another type of sensor shown in FIG. 3E is a triangulation positioner243. A “triangulation positioner” is a type of sensor that sensesposition. The depicted triangulation positioner 243 includes a sensorUID, additional stored sensor data, and environmental measurements asdiscussed above.

In some embodiments, a triangulation positioner (also known as a globalpositioning system (GPS) receiver) receives clock data transmitted byone or more geostationary satellites (a satellite in a known or knowableposition) and/or one or more ground based transmitters (also in known orknowable positions), compares the received clock data, and computes a“position calculation”. The position calculation may be included in oneor more sensor information packets as environmental measurements.

In another embodiment, a triangulation positioner comprises one or morecameras or image-analyzers that receive emitted or reflected light orheat, and then analyzes the received images to determine the location ofan individual or sensor. Although a triangulation positioner maytransmit data wirelessly, it is not a RF location tag because it doesnot transmit blink data or a tag signal that can be used by a receiverhub/locate engine 108 to calculate location. In contrast, atriangulation positioner senses position and computes a positioncalculation that may then be used as environmental measurements by thereceiver hub/locate engine 108.

In one embodiment, a triangulation positioner could be combined with aRF location tag or reference tag (not shown). In such embodiments, thetriangulation positioner could compute and transmit its positioncalculation via the RF location tag to one or more receivers. However,the receiver hub/locate engine would calculate tag location based on theblink data received as part of the tag signal and not based solely onthe position calculation. The position calculation would be consideredas environmental measurements and may be included in associated sensorinformation packets.

As will be apparent to one of ordinary skill in the art, positioncalculations (e.g., GPS receiver position calculations) are not asaccurate as the location calculations (e.g., UWB waveform based locationcalculations) performed by receiver hub/locate engines structured inaccordance with various embodiments of the invention. That is not to saythat position calculations may not be improved using known techniques.For example, a number of influences, including atmospheric conditions,can cause GPS accuracy to vary over time. One way to control this is touse a differential global positioning system (DGPS) comprising one or anetwork of stationary triangulation positioners that are placed in aknown position, and the coordinates of the known position are stored inmemory as additional stored sensor data. These triangulation positionersreceive clock data from geostationary satellites, determine a positioncalculation, and broadcast a difference between the position calculationand the stored coordinates. This DGPS correction signal can be used tocorrect for these influences and significantly reduce location estimateerror.

Another type of sensor shown in FIG. 3E is a proximity detector 253. A“proximity detector” is a type of sensor that senses identity within anarea (e.g., a local area) that is small with respect to the monitoredarea 100 of FIG. 1. Many different ways of sensing identity (e.g., aunique ID or other identifier for a sensed object or individual) wouldbe apparent to one of ordinary skill in the art in view of thisdisclosure including, without limitation, reading a linear bar code,reading a two-dimensional bar code, reading a near field communication(NFC) tag, reading a RFID tag such as a UHF tag, HF tag, or lowfrequency tag, an optical character recognition device, a biometricscanner, or a facial recognition system.

In some embodiments, a proximity detector senses an attribute of anindividual (or an individual's wristband, tag, label, card, badge,clothing, uniform, costume, phone, ticket, etc.). The identity sensed bya proximity detector may be stored locally at the proximity detector 253as shown and transmitted as environmental measurements via one or moresensor information packets to a sensor receiver 166.

In some embodiments, a proximity detector 253 may have a definedposition, which is often stationary, and may be associated with alocation in the monitored area 100 of FIG. 1. For example, a proximitydetector 253 could be located at a finish line of a race track, anentrance gate of a stadium, with a diagnostic device, at a goal line orgoal post of a football field, at a base or home plate of a baseballdiamond, or a similar fixed location. In such embodiments where theproximity detector is stationary, the position coordinates of theproximity detector and a sensor UID could be stored to a monitored areadatabase (not shown) that is accessible by one or more of the receivers106, 166, the receiver hub/locate engine 108, and/or other components ofthe receiver processing and analytics system 110. In embodiments wherethe proximity detector is movable, a position calculation could bedetermined with a triangulation positioner, or the proximity detectorcould be combined with a RF location tag and located by the receiverhub/locate engine 108. While shown as separate fields for illustrationpurposes in FIG. 3E, identify information and position calculation couldcomprise part of the additional stored sensor data, the environmentalmeasurements, or both.

In one embodiment, the proximity detector could be associated with areference tag (e.g., tag 104 of FIG. 1) whose position is recorded inthe monitored area database. In other embodiments, the proximitydetector is movable, such that it may be transported to where it isneeded. For example, a proximity detector 253 could be located on amedical cart, first down marker, a diagnostic device, goal post, orcarried by a paramedic or security guard. In an embodiment where theproximity detector 253 is movable it would typically be associated witha RF location tag or triangulation positioner so that location (for a RFlocation tag) or position (for a triangulation positioner) can bedetermined at the time identity is sensed.

In the embodiment where the proximity detector includes a RF locationtag, the receiver hub/locate engine 108 would locate the associated RFlocation tag, and the tag data/sensor data filter 112 would associatethe tag location data for the associated RF location tag as the positionof the proximity detector, while determining the identity of anassociated individual from any received sensor information packets. Inthe alternate embodiment where the proximity detector includes atriangulation positioner, the triangulation positioner would compute aposition calculation that could be stored as additional stored sensordata and/or environmental measurements, and transmitted as one or moresensor information packets. In one embodiment, sensor informationpackets for a proximity detector may include both sensed identityinformation and a position calculation.

Another type of sensor shown in FIG. 3E is a proximity label 263. Aproximity label has a fixed position and an identification code (e.g., asensor UID). The proximity label 263 may further comprise additionalstored sensor data as shown. The depicted proximity label 263 isconfigured to be read by proximity detector 253. In some embodiments,proximity detector 253 may be further configured to write information toproximity label 263.

A proximity label 263 may be a sticker, card, tag, passive RFID tag,active RFID tag, NFC tag, ticket, metal plate, electronic display,electronic paper, inked surface, sundial, or otherwise visible ormachine readable identification device as is known in the art. Thecoordinates of the position of the proximity label 263 are stored suchthat they are accessible to the receive hub/locate engine 108. Forexample, in one embodiment, the position coordinates of a proximitylabel 263 could be stored in a field database or monitored area databaseaccessible via a network, or stored locally as additional stored data inthe proximity detector 253.

In some embodiments, a position of the proximity label 263 is encodedinto the proximity label 263 itself. For example, coordinates of aposition of the proximity label 263 could be encoded into a passive RFIDtag that is placed in that position. As another example, the coordinatesof a position of the proximity label 263 could be encoded into a printedbarcode that is placed in that position. As another example, a proximitylabel 263 comprising a NFC tag could be encoded with the location “endzone”, and the NFC tag could be placed at or near an end zone at Bank ofAmerica stadium. In some embodiments, the stored coordinates of theproximity label 263 may be offset from the actual coordinates of theproximity label 263 by a known or determinable amount.

In one embodiment, a proximity label 263 such as an NFC tag may beencoded with a position. When a sensor such as a proximity detectorapproaches the NFC tag it may read the position, then transmit theposition in a sensor information packet to the sensor receiver 166′ andeventually to the receiver hub/locate engine 108. In another embodiment,a proximity label 263 such as a barcode label may be encoded with anidentification code. When a smartphone with a proximity detector (suchas a barcode imager) and a triangulation positioner (such as a GPS chip,GPS application, or similar device) approaches the barcode label it mayread the identification code from the barcode, determine a positioncalculation from received clock data, then transmit the identity and theposition calculation to sensor receiver 166′ and eventually to thereceiver hub/locate engine 106 as part of one or more sensor informationpackets.

In the depicted embodiment, triangulation positioner 243 and proximitydetector 253 are each configured to transmit sensor signals carryingsensor information packets to sensor receiver 166′. The depicted sensors243, 253, like any sensor discussed herein, may transmit sensor signalsvia wired or wireless communication protocols. For example, anyproprietary or standard wireless protocol (e.g., 802.11, Zigbee, ISO/IEC802.15.4, ISO/IEC 18000, IrDA, Bluetooth, CDMA, or any other protocol)could be used for the sensor signals. Alternatively or additionally, anystandard or proprietary wired communication protocol (e.g., Ethernet,Parallel, Serial, RS-232, RS-422, USB, Firewire, I²C, etc.) may be used.Similarly, sensor receiver 166′, and any receiver discussed herein, mayuse similar wired and wireless protocols to transmit receiver signals tothe receiver hub/locate engine.

In one embodiment, upon receiving sensor signals from the triangulationpositioner 243 and the proximity detector 253, the sensor receiver 166′may associate some or all of the data from the received sensorinformation packets with other data stored to the sensor receiver 166′,or with data stored or received from other sensors (e.g., sensor 203),diagnostic devices 233, RF location tags 102, or RF reference tags 104.Such associated data is referred to herein as “associated sensor data”.In the depicted embodiment, the sensor receiver 166′ is configured totransmit some or all of the received sensor information packets and anyassociated sensor data to the receiver hub/locate engine 108 at part ofa sensor receiver signal.

In one embodiment, a smartphone comprising a proximity detector (such asa barcode imager) and a triangulation positioner (such as a GPS chip)may associate an identification code determined from a barcode with aposition calculation from received clock data as associated sensor dataand transmit a sensor information packet that includes such associatedsensor data to the receiver hub/locate engine 108. In anotherembodiment, the smartphone could transmit a first sensor informationpacket including the identification code and the smartphone's uniqueidentifier to another sensor receiver, the smartphone could transmit asecond sensor information packet including the position calculation andthe smartphone's unique identifier to the sensor receiver, and thesensor receiver could associate the position calculation with theidentification code based on the common smartphone unique identifier andtransmit such associated sensor data to the receiver hub/locate engine108. In another embodiment, the sensor receiver could determine a firsttime measurement associated with the first sensor information packet anda second time measurement associated with the second sensor informationpacket that, in conjunction with the sensor UID, could be used, by thereceiver hub/locate engine 108, to associate the first sensorinformation packet with the second sensor information packet.

In one embodiment, the receiver hub/locate engine 108 receives receiversignals from the receiver 106 and sensor receiver signals from thesensor receivers 166, 166′. In the depicted embodiment, receiver 106 mayreceive blink data from the RF location tag 102 and transmits to thereceiver hub/locate engine 108 some or all of the blink data, perhapswith additional time measurements or signal measurements. In someembodiments, time measurements or signal measurements may be based on atag signal received from a RF reference tag (e.g., reference tag 104 ofFIG. 1). The receiver hub/locate engine 108 collects the blink data,time measurements (e.g. time of arrival, time difference of arrival,phase), and/or signal measurements (e. g. signal strength, signaldirection, signal polarization, signal phase) from the receivers 106 andcomputes tag location data for the tags 102 as discussed above inconnection with FIG. 1. In some embodiments, the receivers 106 may beconfigured with appropriate RF filters, such as to filter outpotentially interfering signals or reflections proximate the field ofplay or other area to be monitored.

The receiver hub/locate engine 108 may also access stored data or clockdata from local storage and from a network location. The receiverhub/locate engine 108 uses this information to determine tag locationdata for each RF location tag. It may also associate data derived orextracted from tag signals transmitted from one or more RF location tagswith information or data derived or extracted from sensor signalstransmitted from one or more sensors.

In addition to the TOA or TDOA systems previously described, otherreal-time location systems (RTLS) such as received signal strengthindication based systems could potentially be implemented by a receiverhub/locate engine 108. Any RTLS system using RF location tags, includingthose described herein, could require considerable processing by thereceiver hub/locate engine 108 to determine the tag location data fromthe blink data received from the tags. These may require timemeasurement and/or signal measurement in addition to blink data, whichpreferably includes a tag UID. In contrast, in other systems, such asglobal position systems (GPS) systems, location data is determined basedupon the position calculation transmitted from a GPS transmitter (alsoreferred to as a GPS receiver or GPS tag) which includes calculatedinformation about the location where the tag was positioned (i.e.,coordinates determined at the tag via satellite signal triangulation,etc.) when the position calculation was determined or stored. Thus, GPSinformation typically refers to additional information that istransmitted along with a GPS transmitter ID before the transmission isreceived by a sensor receiver.

A GPS host device or back-end server may receive the GPS information andsimply parse the position calculation (as opposed to calculating theposition information at the host device) and the GPS transmitter ID intoa data record. This data record may be used as a GPS positioncalculation, or it could be converted to a different coordinate systemto be used as a GPS position calculation, or it could be processedfurther with DGPS information to be used as a GPS position calculation.

Returning to FIG. 3C, the depicted RF location tag 202 is used to convey(sometimes called backhaul) sensor information packets to a receiver106. In some embodiments, while not shown, multiple sensors 203 maytransmit sensor signals carrying sensor information packets to RFlocation tag 202. Such received sensor information packets may beassociated with blink data that is transmitted to receiver 106.

In one embodiment, the receiver hub/locate engine 108 may parse sensorinformation packets from received tag data packets and associate suchsensor information packets with the RF location tag 202 that transmittedthe sensor information packet. Thus, the receiver hub/locate engine 108may be able to determine tag location data, which may comprise alocation and other data (e.g., tag data, tag UID, tag-individualcorrelator, sensor-individual correlator, additional stored sensor data,environmental measurements, tag-sensor correlator, identity information,position calculation, etc.) from one or more tags or sensors. Such dataand information may be transmitted to the receiver processing andanalytics system 110.

In some embodiments, once the receiver hub/locate engine 108 determinesa location estimate of a RF location tag 102 at the time epoch of thetag signal, the receiver hub/locate engine 108 can also associate alocation estimate with the tag data packet included in the blink data ofsuch tag signal. In some embodiments, the location estimate of the tagsignal may be used as tag location data for the tag data packet. In someembodiments a Geographical Information System (GIS) may be used by thereceive hub/locate engine 108 to refine a location estimate, or to map alocation estimate in one coordinate system to a location estimate in adifferent coordinate system, to provide a location estimate for the tagdata packet.

In one embodiment, the location estimated for the tag data packet may beassociated with any data in the tag data packet, including a tag UID,other tag data, and, if included, one or more sensor informationpackets, including sensor UID, additional stored sensor data, andenvironmental measurements. Since environmental measurements may includea position calculation from a triangulation positioner (e.g., a GPSdevice), the receiver hub/locate engine 108 could parse the positioncalculation and use it to refine a location estimate for the tag datapacket.

Preferably, the receiver hub/locate engine 108 may access an individualdatabase to determine tag-individual correlators or sensor-individualcorrelators. Individual data (e.g., an individual profile) may be storedin a server, in tag memory, in sensor memory, or in other storageaccessible via a network or communication system, including tag data oradditional stored sensor data as explained previously.

In some embodiments, by comparing data accessed using asensor-individual correlator, the receiver hub/locate engine 108 mayassociate an individual with a sensor information packet received from asensor, and/or may associate an individual with such sensor. Because thereceiver hub/locate engine 108 may associate a sensor position estimatewith a sensor information packet, the receiver hub/locate engine 108 mayalso estimate an individual position for the associated individual.

In another embodiment, by comparing data accessed using a tag-sensorcorrelator, the receiver hub/locate engine 108 may associate a sensorwith a tag data packet received from a RF location tag 102. Because thereceiver hub/locate engine 108 may associate a location estimate with atag data packet, the receiver hub/locate engine 108 may also create asensor location estimate for the associated sensor. By comparing alocation estimate for a RF location tag with a sensor location estimateor a sensor position estimate, the receiver hub/locate engine 108 mayassociate a RF location tag with a sensor, or may associate a tag datapacket with a sensor information packet. The receiver hub/locate engine108 could also determine a new or refined tag-sensor correlator based onthis association.

In still another embodiment, by comparing a location estimate for a RFlocation tag with an individual location estimate or an individualposition estimate, the receiver hub/locate engine 108 may associate a RFlocation tag with an individual, or may associate a tag data packet withan individual. The receiver hub/locate engine 108 could also determine anew or refined tag-individual correlator based on this association.

In one embodiment, by comparing a location estimate for a sensor with anindividual location estimate or an individual position estimate, thereceiver hub/locate engine 108 may associate a sensor with anindividual, or may associate a sensor information packet with anindividual. The receiver hub/locate engine 108 could also determine anew or refined sensor-individual correlator based on this association.

Data derived or extracted from tag signals transmitted from one or moreRF location tags is referred to herein as “tag derived data” and shallinclude, without limitation, tag data, tag UID, tag-individualcorrelator, tag-sensor correlator, tag data packets, blink data, timemeasurements (e.g. time of arrival, time difference of arrival, phase),signal measurements (e. g., signal strength, signal direction, signalpolarization, signal phase) and tag location data (e.g., including taglocation estimates). Tag derived data is not derived by the RF locationtag, but rather, is derived from information transmitted by the RFlocation tag. Information or data derived or extracted from sensorsignals transmitted from one or more sensors is referred to herein as“sensor derived data” and shall include, without limitation, sensor UID,additional stored sensor data, sensor-individual correlator,environmental measurements, sensor information packets, positioncalculations (including sensor position estimates), positioninformation, identity information, tag-sensor correlator, and associatedsensor data. Data derived or extracted from stored individual data isreferred to herein as “individual profile information”, “participantprofile information”, or simply “profile information” and shall include,without limitation tag-individual correlator, sensor-individualcorrelator, identity information, name, uniform number and team,biometric data, tag position on individual. In various embodiments, thereceiver hub/locate engine 108 may transmit tag derived data, sensorderived data, individual profile information, various combinationsthereof, and/or any information from the GIS, the field database, themonitored area database, and the individual database to the receiverprocessing and analytics system 110.

Example Receiver Hub and Receiver Processing and Distribution System

FIG. 4 illustrates an exemplary system 300 for providing performanceanalytics in accordance with some embodiments of the present invention.The depicted performance analytics system 300 may be distributed in areceiver hub 108 and a receiver processing and distribution system 110of the type depicted in FIG. 1. For example, locate engine 302 may bepart of the receiver hub 108 with the tag ID/Filter 304 through eventengine 322 forming part of the receiver processing and distributionsystem 110. In alternative embodiments, the performance analytics system300 may be housed or located in a single housing or unit. In still otherembodiments, the performance analytics system 300 may be distributedamong multiple additional housings or units depending upon theapplication and other design parameters that will be apparent to one ofordinary skill in the art in view of this disclosure.

The performance analytics system 300 of FIG. 4 may include a pluralityof tags 102, and optional sensors 203, associated with participants(e.g., players, officials, balls, field markers, etc.), a plurality ofreceivers 106 positioned within a monitored environment, a receiverhub/locate engine 302, one or more filters 304, a plurality ofdatabases, a plurality of processing engines, and a plurality of outputsystems. While only one type of receiver 106, other types of receivers,e.g., sensor receivers 166, 166′ of FIG. 3E, may be used in accordancewith the embodiments illustrated by FIG. 4. The one or more databasesmay include databases for tag identifiers 354, player roles 308, playerdynamics or kinetics models 310, GIS data or a GIS database 313, fielddata or a field knowledge database 314, formation models 316, playmodels 320, official roles 326, official models 328, ball models 332,weather data 375, and the like. The plurality of processing engines mayinclude a player dynamics engine 306, a team formation engine 312, aplay engine 318, an event engine 322, an official dynamics engine 324, afield marker engine 334, a ball engine 330, and a model generationengine 338, or the like. The system 300 may further include a game clock380 and a universal clock 385.

In an exemplary performance analytics system 300, such as illustrated inFIG. 4, the plurality of tags 102 (and sensors 203) may be attached to aparticipant as discussed in connection with FIGS. 2A-C. In someembodiments, the plurality of tags 102 and/or sensors 203 may beactivated and deactivated as needed, such as before and after a game orwhen damaged or to replace batteries, power suppliers, local memory,etc. Each of the tags 102 may transmit a tag signal, which may includetag derived data, which is received by one or more of the receivers 106.In some embodiments, the receivers 106 may be configured withappropriate RF filters, such as to filter out potentially interferingsignals or reflections proximate the field of play or other environmentto be monitored.

Each of the receivers 106 may receive tag derived data from the tags 102and transmit the tag derived data to the receiver hub/locate engine 302.The receiver hub/locate engine 302 collects the tag derived data fromthe receivers 106 and computes tag location data (based on the blinkdata) for the tags 102 as discussed above in connection with FIG. 1.

In the depicted embodiment, each of the receivers 106 receives sensorderived data from sensor signals transmitted by sensors 203. In otherembodiments, sensor receivers (e.g., sensor receivers 166, 166′ of FIG.3E) may transmit sensor signals comprising sensor derived data to thereceiver hub/locate engine 302.

The tag location data, tag derived data, and sensor derived data may beprovided from the receiver hub/locate engine 302 to a tag ID/filter 304that determines the type of participant associated with each receivedunique tag ID (and/or sensor ID) and routes the associated tag deriveddata (and optionally, other received tag/sensor derived data) to one ormore engines associated with such participant type (e.g., player, ball,official, field marker, etc.). In one embodiment, the tag ID/filter 304performs this routing, at least in part, by correlating the receivedunique tag ID (and/or sensor ID) to profile data or prior correlations(i.e., tag ID No. 0047 is correlated to participant JohnSmith—quarterback, sensor ID No. 12459 is correlated to MarcusHenderson—official, etc.) that may be stored to a tag/sensoridentification database 354 (i.e., tag-individual correlators,sensor-individual correlators, tag-sensor correlators, etc.). In someembodiments, the receivers 106 may also receive sensor derived data forother sensors 203, such as through the tags 102 or through separatetransmission means.

In one embodiment, perhaps in connection with the player illustration ofFIG. 2A, the tag ID/filter 304 identifies tag location data associatedwith a player and thus routes such data to a player dynamics engine 306for further processing. The player dynamics engine 306 is disposed incommunication with a player role database 308, which comprises playerrole data correlating tag and sensor UIDs to player profiles (e.g.,individual profile information) including, without limitation, whichroles (e.g., quarterback, running back, flanker, slot receiver, tightend, left tackle, left guard, center, right guard, right tackle,defensive end, defensive tackle, nose tackle, inside linebacker, outsidelinebacker, free safety, strong safety, cornerback kicker, punter, etc.)the players perform during a game.

The player dynamics engine 306 may also be disposed in communicationwith a dynamics/kinetics model database 310. The player dynamics engine306 may compare the tag location data, other tag and sensor deriveddata, and player role data to player dynamics/kinetics models todetermine aspects of the player dynamics or movement kinetics. Thedynamics/kinetics model database 310 may comprise models of differentaspects or dimensions that may be based on past player location data orother data generated by the model generation engine 338 as discussedbelow. The models may include, without limitation, models for aparticular player profile (e.g., John Smith), a player type (e.g.,quarterback), a player type for a particular team (e.g., a quarterbackfrom the Chicago Wizards), a player type for a particular formation(e.g., a quarterback in a spread offense), and the like. Such models mayconsider all three dimensions (x, y, z) of the tag location data foreach tag (e.g., 102 of FIG. 2A) and may further consider different tagposition arrays (e.g., two tag implementations—one proximate eachshoulder as in FIG. 2A, eleven tag implementations—one proximate eachshoulder, one proximate each elbow, one proximate each hand, oneproximate each knee, one proximate each foot, and one proximate thehead).

In one embodiment, the player dynamics engine 306 determines amulti-dimensional player location per unit time (e.g., participantlocation data) for each player based on the tag location data, other tagand sensor derived data, the player role data, and the playerdynamics/kinetics models. Such multi-dimensional player location mayinclude relative location of the player relative to the field of play,and/or general orientation of the player (e.g., standing, squatting,laying the ground, sitting, etc.) such as by correlating location dataand other tag and sensor derived data.

The player dynamics engine 306 uses the real time tag location datastream from the locate engine 302, as well as the player role database308 to provide accurate information about what a particular player isdoing in real time (or near real time). The player dynamics engine 306may further use other tag and sensor derived data, received from thelocate engine 302 in the depicted embodiment, to aid in determining notonly where the player is, but also how that player's location ischanging with time, velocity, acceleration, deceleration, orientation,or the like. The player dynamics engine 306 outputs multi-dimensionalplayer location information per unit time (e.g., participant locationdata).

In one embodiment, sensor derived data may comprise accelerometer datathat may indicate that a player (or portion of a player) is acceleratingor decelerating. In addition to the variety of other uses that will beapparent to one of ordinary skill in the art in view of this disclosure,the accelerometer data may be used to improve location accuracy for thesystem. For example, in circumstances where the real time tag locationdata stream erroneously suggests (perhaps due to interference, multipatheffects, signal reflections, signal losses due to line-of-sightblockages, etc.) that one of the possible locations for the player is 10feet away from a prior location, the accelerometer data could be used toconfirm that the player (or accelerometer affixed portion of the player)did not experience an acceleration sufficient to move that distance inthe amount of time provided.

In some embodiments, sensor derived data may comprise time-of-flightsensor data, which may indicate distances between participants (e.g.,distance of a player to other players, officials, the ball, etc.) orother objects. In applications involving complex tagged object movementssuch as, the example football application discussed herein,time-of-flight sensor data may be used to enhance the location accuracyof the system especially in circumstances where one or more tags orsensors are temporally unable to effectively transmit their data to oneor more receivers. For example, in one embodiment, a tag positionedwithin the ball may appear to the system as not moving because therunning back carrying the ball has run into a group of other players andthe bodies of such other players are actually blocking the line-of-sighttransmissions of the ball tag. In this embodiment, time-of-flightsensors positioned on the group of other players may be repeatedlydetermining and transmitting to one or more receivers the relativedistance between such time-of-flight sensors and the ball or ballcarrier. In this regard, the system may determine that the ball is nolonger at the ten yard line (i.e., the point where the system lastreceived a transmission directly from the ball tag) but rather hasadvanced toward the opponent's end zone to the six yard line. This andother similar techniques may be used alone or in combination with othertag and sensor derived data (e.g., accelerometer data, etc.) to create atype of mesh network that may adapt to temporary or sustainedline-of-sight blockages and improve the accuracy of locationdeterminations, formation determinations, play determinations, etc.

In some embodiments, the player dynamics engine 306 outputsmulti-dimensional player location information per unit time to an eventengine 322. In some embodiments, the multi-dimensional player locationinformation may include a ranked or weighted list of probable playerlocations while, in other embodiments, the multi-dimensional playerlocation information includes only a top, or most probable, playerlocation. This information may be used by the event engine 322 todetermine a number of important player events. For example, themulti-dimensional player location information may be used to indicatethat a player was tackled (i.e., experienced a rapid deceleration andtransited from a standing to a laying position) and is subsequentlylimping (e.g., tag and/or sensor data from tags/sensors proximate theplayers feet indicate a change in the gait of the player). In suchexample, the event engine 322 may be configured to transmit an alert(e.g., via text message, email, or the like) to an athletic trainer tohave the player checked-out or treated.

The player dynamics engine 306 may further output the multi-dimensionalplayer location information per unit time (e.g., participant locationdata) to a team formation engine 312. The team formation engine 312 isdisposed in communication with a formation models database 316 thatcontains models of various formations (e.g., offensive formations,defensive formations, special teams formations, etc.) defined for therelevant sport or activity (e.g., football in the depicted embodiment).The models of various formations may be derived from multi-dimensionalplayer location information collected during prior games, practices,etc., (e.g., learned by the system) or as input by one or more teams,such as by using model generation engine 338, historical data store 336,and/or team analytics engine 346.

The team formation engine 312 is further disposed in communication witha field data database 314 to assist in determining the likely teamformations. The field data database 314 may comprise, withoutlimitation, survey data for the field (e.g., various distances orcoordinates from reference tag(s) or other marker to yard lines, endzones, goal posts, boundaries, benches, locker rooms, spectator areas,other zones of interest, etc.).

In one embodiment, the team formation engine 312 determines one or moreformations (e.g., a probable formation or a ranked or weighted list ofprobable formations) based at least in part on the field data, themulti-dimensional player location information (which may include the tagderived data and/or sensor derived data), and the formation models. Theteam formation engine 312 may hypothesize the received multi-dimensionalplayer location data against models of every known formation todetermine a probable formation or a ranked or weighted list of probableformations. The team formation engine 312 is thus configured todetermine and output a data stream of formations versus time, whichconsiders how various formations change and may be used by downstreamengines to determine various events including the occurrence of a play.

In one embodiment, the team formation engine 312 may assign weights tothe received multi-dimensional player location data (i.e., participantlocation data), other types of tag derived data and/or sensor deriveddata, and/or to the formation models when determining a specificformation or ranked list of probable formations. For example, in oneembodiment, the team formation engine 312 may be configured to assign agreater weight to the position of the ball (which should remainstationary for a period of time as formations are being established,i.e., at the beginning of a play) than to the position of an official(which may move to some degree as formations are forming). In anotherembodiment, the team formation engine 312 may be configured to assign agreater weight to the location of the tight-end (which may indicate thestrong side of a formation) than to the location of a left guard (whoselocation seldom effects formation determination). In still anotherembodiment, the team formation engine 312 may be configured to assign agreater weight to sensor derived data associated with an accelerometerpositioned proximate an official's wrist (which may indicate winding ofthe play clock that often triggers the period during which formationsought to be forming) than to the location of any player.

In one embodiment, the team formation engine 312 outputs the data streamof formations versus time (e.g., formation data) to the play engine 318.The play engine 318 may also receive the output data stream (e.g.,multi-dimensional player location information versus time) from theplayer dynamics engine 306. The play engine 318 is disposed incommunication with a play models database 320. The play models database320 may include play models (e.g., known formation shifts or movementsover time). Such play models may be programmatically learned by thesystem (e.g., based on actual movements of players tracked by thesystem) or manually entered through an interface or other tool (e.g.,perhaps through the model generation engine 338). In this regard, theplay models database 320 may include historical plays executed by teams,potential/future plays from a team game plan or playbook, or otherhistorical data (e.g., from historical data store 336).

In one embodiment, the play engine 318 may take the formations versustime data stream from the formation engine 312, the play models, and theplayer dynamics data stream (which may include tag location data and/orother tag and sensor derived data) to determine whether a play isforming, a play has started, a play is in progress, or a play has ended.For example, the play engine 318 may determine that it is most likelythat a pre-snap formation at the line of scrimmage has occurred (e.g.,an offensive team has aligned in a “pro set” formation and a defensiveteam has aligned in a “3-4” formation) indicating a play is about tobegin. The play engine 318 may thereafter determine that the offensiveand defensive players have begun rapidly accelerating towards and acrossa line of scrimmage thereby indicating that a play has begun. The playengine may further determine that an offensive player has been tackledby a defensive player thereby indicating that a play has concluded.

In some embodiments, the play engine 318 may use assigned weights (orassign weights) to the received data (e.g., the tag derived data, thesensor derived data, the multi-dimensional player location data, theformations data, officials data, etc.) for use in analyzing the data anddetermining the most probable play events. For example, the play engine318 may determine that data for particular participants (e.g., a leftguard) has a lower relevance for a particular formation (e.g., a pro setoffensive formation) and assign a lower weight to that data during theanalysis than to another participant (e.g., the ball, the quarterback, awide receiver, etc.).

In some embodiments, the play engine 318 is disposed in communicationwith an official dynamics engine 324 to further improve the playdetermination accuracy of the system. The official dynamics engine 324may provide data about the movements, actions, positions of an official,which the play engine 318 may use when determining a probable playand/or the status of a play. For example, as discussed in connectionwith FIG. 2B above, an official may be provided with wrist basedaccelerometers or other sensors (e.g., a whistle sensor), which may beused to flag the beginning or ending of a given play based on themovement or action of an official (e.g., rotating an arm to wind theplay clock, indicate touchdown with two arms raised, blow a whistle,etc.).

The play engine 318 may analyze how the team formations occur and howthey break up to determine both start and stop of a play (e.g., start ofplay event, end of play event, etc.). For example, the play engine 318may determine that offensive and defensive formations coalescedproximate a line of scrimmage and then broke up with various receiversheading towards the defensive team's end zone, there was all kinds ofactivity around a quarterback which eventually dissipated, and thatdefense players were tracking one of the receivers downfield until thereceiver crossed into the end zone and an official raised his arms. Theplay engine 318 may determine that this participant activity best fits aplay model whereby a ball was thrown and caught by a receiver who thenscored a touchdown thereby ending the play.

In some embodiments, the play engine 318 may hypothesize the receivedmulti-dimensional player location data (e.g., participant location data)and the data stream of formations versus time against models of everyknown play to determine a probable play or a ranked list of probableplays. The play engine 318 is thus configured to determine and output adata stream of plays versus time, which may be communicated to the eventengine 322.

In some embodiments, the tag ID/filter 304 may determine that receivedtag derived data and/or sensor derived data corresponds to one or moreofficials. Such official correlated tag/sensor derived data is routed tothe official dynamics engine 324. The official dynamics engine 324 isdisposed in communication with an official roles database 326, whichcomprises official roles data correlating tag and sensor IDs (or othertag/sensor individual correlators) to official profiles including,without limitation, which roles (e.g., referee, umpire, head linesman,line judge, back judge, field judge, side judge, etc.) the officialsperform during a game.

The official dynamics engine 324 may also be disposed in communicationwith a dynamics/kinetics model database 328. The official dynamicsengine 324 may compare the tag location data, other tag/sensor deriveddata, and official role data to official dynamics/kinetics models todetermine aspects of the official dynamics or movement kinetics. Thedynamics/kinetics model database 328 may comprise models of differentaspects or dimensions that may be based on past official location dataor other data generated by the model generation engine 338 as discussedbelow. The models may include, without limitation, models for aparticular official profile (e.g., Ralph Stevens), an official type(e.g., referee), an official type for a particular formation (e.g., areferee positioned during a kickoff), and the like. Such models mayconsider all three dimensions (x, y, z) of the tag location data foreach tag (e.g., 102 of FIG. 2B) and may further consider different tagposition arrays (e.g., two tag implementations—one proximate eachshoulder as in FIG. 2B, eleven tag implementations—one proximate eachshoulder, one proximate each elbow, one proximate each hand, oneproximate each knee, one proximate each foot, and one proximate thehead).

In one embodiment, the official dynamics engine 324 determines amulti-dimensional official location per unit time for each officialbased on the tag location data, other tag and sensor derived data, theofficial role data, and the official dynamics/kinetics models. Suchmulti-dimensional official location may include (1) a relative locationof the official relative to the field of play, (2) a general orientationof the official (e.g., standing, squatting, laying the ground, sitting,etc.), and (3) a specific orientation of the official (e.g., armsraised, arms at hips, one hand grasping the wrist of the other arm,etc.).

The official dynamics engine 324 uses the real time tag location datastream from the locate engine 302, as well as the official role database326 to provide accurate information about what a particular official isdoing in real time (or near real time). The official dynamics engine 324may further use tag and sensor derived data, received from the locateengine 302 in the depicted embodiment, to aid in determining not onlywhere the official is, but also how that official's location is changingwith time, velocity, acceleration, deceleration, orientation, or thelike. For example, in one embodiment, the sensor derived data maycomprise accelerometer data that may indicate that an official (orportion of an official) is accelerating or decelerating. The officialdynamics engine 324 outputs multi-dimensional official locationinformation per unit time. Such multi-dimensional official locationinformation may include information regarding the official's location,how the location is changing with time, orientation of the official,motions or gestures of the official, or the like.

In some embodiments, the tag ID/filter 304 may determine that receivedtag and/or sensor derived data corresponds to the game ball (e.g., aball such as the ball shown in FIG. 2C, which is used or capable of usein the field of play). Such ball correlated tag/sensor derived data isrouted to the ball dynamics engine 330. While the ball engine 330 is notshown in communication with a “roles” database as in the case of some ofthe other processing engines, one of ordinary skill in the art willreadily appreciate some ball role data may be used, such as a ball ID orthe like, indicating that the received tag/sensor derived data isassociated with a given ball.

The ball engine 330 may access a ball models database 332, whichcomprises data indicating how location data and other tag and sensorderived data correlates to particular ball events during play. The ballengine 330 may provide information regarding the ball'sposition/location (vertical and/or horizontal), how the location ischanging with time, the velocity of the ball, the rotation of the ball,or the like for determining events during play. The ball engine 330 mayoutput ball data streams to the event engine 322. In some embodiments,although not shown in FIG. 3, the ball engine may also output a datastream to other processing engines for analysis, such as to the playengine 318 for use in determining status of a play.

In some embodiments, the tag ID/filter 304 may determine that receivedtag and/or sensor derived data corresponds to a field marker (e.g.,penalty flags, line of scrimmage markers, yards to gain markers, and thelike). The tag ID/filter may then route such field marker correlatedtag/sensor derived data to a field marker engine 334 for furtherprocessing. The field marker engine 334 may provide informationregarding field marker location, how the location is changing with time,or the like, for determining events during play. The field marker engine334 may output data streams to the event engine 322. In someembodiments, although not shown in FIG. 3, the field marker engine mayalso output a data stream to other processing engines for analysis, suchas to the play engine 318 for use in determining the status of a play.

In some embodiments, a game clock 380 may be provided that is configuredto keep an official time for a game or other tracked activity. Inapplications such as the depicted football application, the game clockis configured to count down from some standard period or quarter length(e.g., 15 minutes) and may be periodically stopped or started by one ormore officials and/or the game operations system 342 as discussed ingreater detailed below. While not separately illustrated in FIG. 3, thegame clock 380 may further include a play clock, shot clock, pitchclock, or the like, which depending upon the application, may also bestarted and stopped by one or more officials and/or the game operationssystem 342.

The universal clock 385 provides a system time for the performance andanalytics system 300. As will be apparent to one of ordinary skill inthe art in view of this disclosure, the universal clock 385 is runningclock for tracking, cataloging, and calibrating system actions,processes, and events. For illustrations purposes, the game clock 380and the universal clock are shown as inputs for the event engine 322;however, in other embodiments, such clocks may provide inputs to any orall of the player dynamics engine 306, the team formation engine 312,the play engine 318, the event engine 322, the official dynamics engine324, the field marker engine 334, the ball engine 330, and the modelgeneration engine 338.

An event engine 322 may receive the outputs from the player dynamicsengine 306, the team formation engine 312, the play engine 318, theofficial dynamics engine 324, the ball engine 330, the field markerengine 334, the weather data store 375, a game clock 380, and auniversal clock 385 to determine events occurring during game play or toperform analytics, including predictive analytics, on game related data.In some embodiments, the event engine 322 determines such events andperforms such analytics by comparing its received inputs to a historicdata store 336 containing past events or analytics. In some embodiments,the event engine 322 outputs event data streams (e.g., one or moreoutput events) to a number of systems including, without limitation, avisualization system 340, a game operations system 342, a camera controlsystem 344, a team analytics system 346, a league analytics system 348,a statistics system 350, an XML feed and/or instant message feed 352, ahistorical data store/engine 336, or other systems as may be apparent toone of ordinary skill in the art in view of this disclosure.

In some embodiments, the event engine 322 may output event data streamsthat include the time delay between tag and/or sensor transmissions andthe determination of the events such that other processes, such as avisualization system, game operations system, etc., may properlycorrelate to different inputs (e.g., video recording versus thedetermined events) so that the different inputs are synchronized. Inother embodiments, the event data streams may include time stamps (gametime stamp, universal time stamp, etc.) for determined events or othersystem processes. In this way, the performance and analytics system 300or some downstream system can determine, inter alia, which events orprocesses occurred in-game (i.e., during a running game or play clock)or out-of-game (i.e., while the game or play clock were stopped).

In various embodiments, the event data streams or output events providedby the event engine may include tag events (e.g., battery lowindication, error indication, etc.), sensor events (e.g., battery lowindication, error indication, etc.), locate engine events (e.g., statusindications, error indications), tag ID/Filter events (e.g., statusindications, error indications), player dynamics engine events (e.g.,status indications, error indications), player events (e.g., playertackled indication, player injured indication, etc.), official dynamicsengine events (e.g., status indications, error indications), officialevents (e.g., official injured indication, etc.), ball engine events(e.g., status indications, error indications), ball events (e.g., newball required indication, etc.), team formation engine events (e.g.,status indications, error indications), team formation events (e.g.,formation type indication, new formation indication, illegal formationindication, etc.), play engine events (e.g., status indications, errorindications), play events (e.g., play type indications such as run,pass, punt, field goal, etc., play results, and in-play or sub-playevents such as bootleg, 3 step drop, 5 step drop, 7 step drop, crossingpattern, hook pattern, fly pattern, drive block, pass block, spin move,swim move, press coverage, zone coverage, etc.), or any other eventsthat may be apparent to one of ordinary skill in the art in view of thisdisclosure. A variety of additional event data streams or output eventsare described in connection with the analytics systems and controlsystems discussed below.

In one embodiment, the event engine 322 outputs an event data stream tothe visualization system 340 that may be used by the visualizationsystem to provide enhanced telecasts or game experiences for televisionbroadcasts, streaming mobile device clients, and other media outlets,gaming systems, and other computer graphics visualization systems. Suchevent data streams may be used to provide enhanced graphics, displays,information, visualizations, and the like. For example, and withoutlimitation, the visualization system 340 may receive real time (or nearreal time) data including, without limitation, player ID, official ID,team ID, formation identifiers, play identifiers, pre-snap playprobabilities, play diagrams, player route data, player speed data,player acceleration data, ball route date, ball speed data, ballacceleration data, player trend information, offensive team trendinformation, defensive team trend information, special teams trendinformation, and other tag and/or sensor derived data. In someembodiments, the visualization system 340 may be configured to provide adynamically configurable interface that may be engaged by a user toselect graphics or areas of focus. For example, in one embodiment, auser may configure the system to display possible passing lanes for aquarterback to his eligible receivers. In still other embodiments, thevisualization system 340 may output a data stream for use in gamingsystems, such as plays, player actions, or the like.

In gaming systems examples, the visualization system 340 may provideoutput of event data that may be configured for display via a gamingconsole or handheld device. Such visualization system outputs may, forexample, provide for incorporating actual or predicted actions of a“live” player into a gaming environment. In some embodiments, thevisualization system may also access stored computer generated or usercreated avatars for use with the event data stream.

In one embodiment, the event engine 322 outputs an event data stream tothe game operations system 342 that may be used by the game operationssystem to coordinate, manage, or assist in the coordination or managingof game operations including, without limitation, the game clock 380(and optionally the play clock), down and distance determination, scoreboard operations, penalty enforcement, and the like. For example, andwithout limitation, the game operations system 342 may receive real time(or near real time) data from the event engine 322 including, withoutlimitation, a clock start indication, a clock stop indication, a playstart indication, a play end indication, a reset play clock indication,a 1^(st) down indication, a 2^(nd) down indication, a 3^(rd) downindication, a 4^(th) down indication, a turnover indication, a yard togain indication, a 5 yard penalty indication, a 10 yard penaltyindication, a 15 yard penalty indication, an end of quarter indication,an end of half indication, and an end of game indication.

Said differently, the event engine 322 may determine a number of eventsthat may be output to the game operations system or other devices. Suchevents may include, without limitation, a ready for play event (e.g., anofficial has spotted the ball at the line of scrimmage and started aplay clock in a football example, a pitcher has received the ball fromhis catcher in a baseball example, or the pins have been set in abowling example), a start of play event (e.g., the ball has been snappedin a football example, the pitcher has begun his pitching motion orwind-up in a baseball example, or a bowler has begun his bowling motionin a bowling example), and an end of play event (e.g., the official hasblown a whistle in a football example, an umpire has called a thirdstrike in a baseball example, or the nine pins have been knocked down ina bowling example). Such events may be used to determine plays,formations, and to output play diagrams (e.g., graphs or plots ofparticipant location versus time from a start of play event to an end ofplay event).

The event engine 322 may be further configured to output a play resultto the game operations system 342 or other device. Such play results mayinclude, for example and without limitation, a gain of twelve yards, aloss of three yards, an interception, a touchdown, a first down, and thelike in football embodiments; a ball, a strike, a fly-out, a single, adouble, a home run, a run scored, and the like in baseball embodiments;and a gutter, a strike, a spare, and the like in bowling embodiments.

As would be apparent to one of skill in the art, the various enginesand/or output systems may include security measures, such as encryption,access permissions, and the like, to secure system inputs and outputs.In some embodiments, the engines and/or output systems may comprisesecurity measures to prevent hacking, jamming, transmissioninterception, etc. to prevent interference from outside parties, such asthird parties attempting to gain information that may be advantageous inwagering, for example.

In one embodiment, the event engine 322 outputs an event data stream tothe camera control system 344 that may be used by the camera controlsystem to engage or transition engagement between one or moretelevision, film, or other cameras to capture game events. For example,and without limitation, the camera control system 344 may receive realtime (or near real time) data including, without limitation, an engageor disengage camera 1 indication, an engage or disengage camera 2indication, an engage or disengage camera 3, . . . and an engage ordisengage camera n indication. In some embodiments, the event engine 322may output camera control indications (e.g., event data) based on realtime (or near real time) game activity (e.g., ball location datasuggests that the ball is closest to a known field of view for camera 4and, thus, an engage camera 4 indication is transmitted to the cameracontrol system 344). In other embodiments, the event engine 322 mayoutput camera control indications (e.g., event data) based in part on aprediction of game activity (e.g., ball position, acceleration, anddirection data suggests that the ball has just left the quarterback'shand and is being passed along a direction and at a velocity indicativeof being caught in the field of view of camera 4 and, thus, an engagecamera 3 indication is transmitted to the camera control system 344). Inother embodiments, the camera control system 344 may provide indicationssuch as to tilt, pan, or zoom in connection with a particular camerabased on event data or predicted actions, or set a location or point ofview based on where a player, formation, or play may be best viewed.

In one embodiment, the event engine 322 outputs an event data stream tothe team analytics engine 346 that may be used to generate real time (ornear real time) analytics (e.g., player performance information,formation performance information, play performance information, andteam performance information) concerning game activity that may beuseful to individual teams. For example, in one embodiment, the teamanalytics engine 346 may use event data to determine actual gameperformance versus playbook design including, without limitation, anevaluation of player routes, offensive, defensive, and special teamsformations, offensive blocking protection schemes, defensive blitzingschemes, and the like.

In another embodiment, the team analytics engine 346 may use event datato determine actual game performance versus historical game performance(such as by using historical data store 336) including, withoutlimitation, an evaluation of game performance (e.g., player, team,offense, defense, special teams, etc.) versus prior year performance,prior game performance, prior quarter performance, prior possessionperformance, prior play performance, and the like. In this regard, aswill be apparent to one of ordinary skill in the art, the team analyticsengine 346 may be used to evaluate performance (e.g., the left tacklehas missed three assignments), identify trends (e.g., the defensive teamconsistently sends a linebacker blitz against a spread offensiveformation), make player substitutions (e.g., a second string left tacklehas performed better historically against the right end of the defenseand thus should be substituted for the starting left tackle), revisegame plans, or provide alerts (e.g., the flanker has experiencedsignificantly reduced speed, acceleration, and performance followingbeing tackled and thus an alert should be generated to the trainingstaff to ensure that such player is medically evaluated).

For example, in one embodiment, a trainer may have a device, such as ahandheld device, tablet, etc., that may receive alerts regarding aparticular player. The trainer may receive background information and/orpast information on a player as well as what change the system hasidentified to cause the alert, such as a change in gait, slower routerunning, etc. The trainer may then be able to evaluate the player andprovide input to the system regarding the player evaluation, such as iffurther attention is required or if the player can return to play. Insome embodiments, such alert and evaluation results may also be providedto the league analysis system, such as for use in determining injurytrends or the like.

In some embodiments, the team analytics engine 346 may be used to alerta team (e.g., coaches) to focus on specific players who are performingsub-par versus their normal (historical) performance, such as by playsor by teams. In some embodiments, the team analytics engine 346 mayfurther output analysis results to the historical data store 336 or thelike, for use in future analysis and/or the building or updating ofvarious models.

In one embodiment, the performance and analytics system is configured toevaluate player performance by correlating at least one tag to theplayer; receiving blink data (and other tag derived data) transmitted bythe at least one tag; determining tag location data based on the blinkdata; receiving player role data; comparing the tag location data toplayer dynamics/kinetics models based at least in part on the playerrole data; determining player location data based on the comparing thetag location data to the player dynamics/kinetics models; anddetermining player performance information based on comparing the playerlocation data to stored player location data. In some embodiments, thestored player location data may be stored to the historical data store336 and may include player location data for the actual player to beevaluated (e.g., Frank Smith, left tackle, #55) and/or player locationdata for another player (e.g., Fred Johnson, left tackle, #65) who playsa similar position to the actual player to be evaluated. In still otherembodiments, the stored player location data may include competitivedata based on the performance of the actual player against an opposingplayer (e.g., the left tackle blocked the right defense end successfullyin five prior match-ups, the defensive back caused a delay by the widereceiver of 2 seconds in running a passing route by applying presscoverage, etc.).

In another embodiment, the performance and analytics system isconfigured to evaluate official performance by correlating at least onetag to the official; receiving blink data (and other tag derived data)transmitted by the at least one tag; determining tag location data basedon the blink data; receiving official role data; comparing the taglocation data to official dynamics/kinetics models based at least inpart on the official role data; determining official location data basedon the comparing the tag location data to the official dynamics/kineticsmodels; and determining official performance information based oncomparing the official location data to stored official location data.In some embodiments, the stored official location data may be stored tothe historical data store 336 and may include official location data forthe actual official to be evaluated and/or official location data foranother official who held a similar position (e.g., referee, umpire,etc.) to the actual official to be evaluated.

In one embodiment, the event engine 322 outputs an event data stream tothe league analytics engine 348 that may be used to generate real time(or near real time) analytics concerning game activity that may beuseful to a league (i.e., a collection of teams). For example, in oneembodiment, the league analytics engine 348 may use event data toimprove game safety by identifying injury trends (e.g., playerconcussions occur at a higher rate when an offensive team runs crossingpassing routes from a spread formation against a 3-4 defense, etc.). Inanother embodiment, the league analytics engine 348 may use event datato evaluate rule changes (e.g., a rule change intended to speed up gameplay is or is not achieving its intended result). In still anotherembodiment, the league analytics engine 348 may use event data toimprove officiating (e.g., determining the accuracy of official calls).In some embodiments, the league analytics engine 348 may further outputanalysis results to the historical data store 336 or the like, for usein future analysis and/or the building or updating of various models.

In one embodiment, the event engine 322 outputs an event data stream tothe statistics engine 350 that may be used to generate real time (ornear real time) statistics concerning game activity. Such statistics mayinclude, without limitation, offensive statistics (e.g., passing,rushing, receiving, turnovers, touchdowns scored, etc.), defensivestatistics (e.g., tackles, sacks, interceptions, turnovers generated,etc.), special teams statistics (e.g., punt length, punt hang time,average return, long return, field goal accuracy, etc.), play diagrams,length of play statistics (e.g., 4.8 second average play, 22 secondaverage pre-snap formation period, etc.), player participationstatistics (e.g., John Smith participation in 42 of 68 offensive plays,etc.), summary statistics (e.g., top scorers, fantasy points, minutes onoffense, etc.), official statistics (e.g., penalties called, locationtracking diagrams per play, etc.) and the like. In some embodiments, thestatistics engine 350 may further output statistics and results to thehistorical data store 336 or the like, for use in future analysis and/orthe building or updating of various models.

In one embodiment, the event engine 322 outputs an event data stream tothe XML feed and/or instant messaging feed engine 352 that may be usedto generate XML or instant messaging data streams that may include livedata such as plays, scoring plays, other scoring info, results, topscorers, summary statistics, or the like.

In one embodiment, the event engine 322 may output an event stream thatmay be used to annotate or tag a game recording, for example, usingvisualization system 340, game operations system 342, or the like. Forexample, in one embodiment, the event engine 322 may flag, tag, orannotate certain events (e.g., plays, penalties, formations, clockstart/stop, etc.) into a video recording or live data stream of a gamefor later playback or analysis. In some embodiments, any eventidentified by the event engine 322 may be flagged, tagged, or annotatedto a video or other data stream to provide for ease of lateridentification. In this regard, various events may be readily searched,identified, stored to a database in an indexed way, and/or analyzed.

In some embodiments, the event engine 322 may determine events occurringproximate one or more play boundaries. For example, using outputs fromthe player dynamics engine 306, the ball engine 330, and the officialdynamics engine 324 the event engine 322 may determine that a touchdownhas been scored (i.e., a player has carried the ball across a goalboundary into the endzone). In particular, the event engine 322 maydetermine that a running back carried the ball (based on location datareceived from the ball engine and the player dynamics engine) across thegoal boundary (based on field data), which was confirmed by the nearestofficial signaling touchdown by raising both arms (based on locationdata received from the official dynamics engine).

In some embodiments, the event engine 322 may output an event datastream to a historical data store/engine 336, which may store datagenerated by the various processing engines over time. The historicaldata store/engine 336 may be accessed by various systems, such as foruse in providing analytics or generating new models. For example,historical data store/engine 336 may provide historical data to modelgeneration engine 338, which the model generation engine 338 may use inlearning (or developing) new play or formation models that should beadded to the respective model databases. In some embodiments, thehistorical data store/engine 336 may be accessed by the analytics andstatistics systems to generate more in-depth analytics or statistics. Insome embodiments, the historical data store 336 may comprise prior eventand tag derived data received by the system for each individual player(e.g., John Smith) and may also comprise player data received from othersources, such as from manual input tools (i.e., such as using a form ortemplate) or external data sources (e.g., other statistics databases,etc.).

In some embodiments, the event engine 322 may output an event datastream that may be used in conjunction with historical results, such asfrom historical data store 336, for determining odds for outcomes ofvarious team matchups. For example, the event data stream and historicalevent data may be analyzed to generate and/or change predicted odds foroutcomes of each play, etc., which may be used in a wagering system orthe like.

In some embodiments, the team analytics system 346 may provide aninterface tool (i.e., perhaps through the model generation engine 338)configured to allow a team to input future plays (i.e., a game plan).Such future plays may be tested against historical data stored to thehistorical data store 336 in order to determine a probability forsuccess. For example, the team analytics system 346 may be configured toallow a team to virtually test an individual play intended to be runfrom a given offensive formation against defenses that were historicallyrun against such offensive formation. As will be apparent to one ofordinary skill in the art in view of this disclosure, the team analyticssystem 346 may be configured to allow a team to virtually test its gameplan against another team, specific players, specific formations,specific blocking protections, specific blitz packages, specific weatherconditions, and the like.

In one embodiment, the team analytics system 346, or any other engine orsystem, may be configured with access security controls (e.g., passwordprotection schemes, etc.) sufficient to limit access to team proprietarydata (e.g., game plan information, player injury data, etc.) toindividual teams. In this regard, game integrity may be preserved byensuring that proprietary data of a first team is not obtained by acompeting second team.

In some embodiments, the event engine 322 and its corresponding outputsystems (i.e., the visualization system 340, the game operations system342, the camera control system 344, the team analytics system 346, theleague analytics system 348, the statistics system 350, the XML feed/IMfeed system 352, and the historical data store/engine 336) may beconfigured to provide different levels of specificity for the outputdata. For example, an individual team may receive output data breakingdown the specific details for each play and the player dynamics for theplay, such that the team may determine the performance of each player inexecuting the specifics of a play versus an intended design. Incontrast, similar yet less detailed output may be provided to all teamssuch as basic play diagrams and standard statistics for the players.

In some embodiments, one or more of the engines shown in FIG. 3, suchas, without limitation, the team formation engine, the play engine, theevent engine, or the like, may output lists or ranked lists of probableoutput events (e.g., locations, formations, plays, events, etc.) fordisplay to a user via a graphical user interface (e.g., PC, tablet,mobile device, etc.) and/or for use by downstream engines or systems. Inother embodiments, the above described engines may select from theranked list of probable events a most probable event, or more simply a“probable event” (e.g., probable location, probable formation, probableplay, probable blocking technique, probable passing route, etc.), thateither has the highest probability indicator among the ranked list orhas a probability indicator above a pre-defined threshold.

In some embodiments, the user may validate or confirm an output event(e.g., a location, a formation, a play, or an event) to improve systemoperation. For example, in one embodiment, the event engine 322 maydetermine that the following events may have occurred each with arespective probability indicator shown in parenthesis: completed pass—12yard gain for the offense (68%); completed pass—10 yard gain for theoffense (21%); incomplete pass—0 yard gain for the offense (19%). Thisranked list may be displayed to an official via a mobile device who mayselect and confirm the correct output event, which in this example isthe completed pass for a 12 yard gain for the offense. In this regard,as will be apparent to one of ordinary skill in the art in view of thisdisclosure, the system may employ a user to break ties or close calls(e.g., probabilities within 10 percent, etc.) or to improve the accuracyof models, input weighting allocations, and the like.

In still other embodiments, the performance and analytics system maydetermine or predict participant locations, formations, plays, or otherevents despite temporary or sustained losses of blink data for one ormore tags (e.g., due to transmission failures associated with multipatheffects, line-of-sight blockages, etc.). For example, in one embodiment,the performance and analytics system: receives first tag location datafor a first participant (e.g., a ball carrier) during a first timeperiod (e.g., an in-play period representing the first 3 seconds of aplay); receives subsequent first tag location data for the firstparticipant during a second time period (e.g., a second in-play periodrepresenting the second 3 seconds of a play); receives second taglocation data for a second participant (e.g., the ball carried by theball carrier) during the first time period; and determines (or predicts)subsequent second tag location data for the second participant duringthe second time period based at least on: the first tag location datafor the first participant during the first time period, the subsequentfirst tag location data for the first participant during the second timeperiod, and the second tag location data for the second participantduring the first time period.

The above determination or prediction may be further improved using tagderived data and sensor derived data. For example, the performance andanalytics system may receive first sensor derived data (e.g.,time-of-flight sensor data or other tag and sensor derived datasuggestive of a relative proximity between the first participant and thesecond participant) for the first participant during the first timeperiod; receive subsequent first sensor derived data for the firstparticipant during the second time period; and determine the subsequentsecond tag location data for the second participant during the secondtime period further based at least on: the first sensor derived data forthe first participant during the first time period, and the subsequentfirst sensor derived data for the first participant during the secondtime period.

In still other embodiments, the above determination or prediction ofsecond participant location may be improved by comparing participantlocation at various times to formation and/or play models. Suchcomparisons may further include field data, and participant role data.For example, if we maintain the above example whereby the firstparticipant is a ball carrier and the second participant is a ball, theperformance and analytics system may determine or predict the locationof the ball (i.e., in circumstances where tag or sensor transmissionsfrom the ball are blocked) during a pre-snap period by determining thatthe ball carrier is aligned in a stationary location in the backfield.By comparing such ball carrier location data to formation models, thesystem may determine that the ball is most likely positioned at the lineof scrimmage proximate the center.

Similarly, in another embodiment, perhaps where the first participant isa quarterback and the second participant is a left guard, theperformance and analytics system may determine or predict the locationof the left guard in any given play or time period based upon comparingmovements of the quarterback to formation and play models. For example,quarterback movement from a snap position to a drop back passingposition may be suggestive that the left guard is positioned in a passblocking position proximate the line of scrimmage. Alternatively,quarterback movement from a snap position to a hand-off position may besuggestive that the left guard is positioned up field of the line ofscrimmage in a run blocking position.

FIG. 5 illustrates example participant (e.g., player) tracking over timein accordance with some embodiments of the present invention. Morespecifically, FIG. 5 illustrates the changing position of an offensiveteam during game action. Such tracking of changing positions may beuseful for various engines of the present system including, withoutlimitation, the player dynamics engine, the formation engine, the playengine, and the event engine. For example, at a first time, t−1 (e.g.,game clock: 12:26, play clock: 38 seconds, universal clock: 16:09:30),the tag location data may indicate that the tracked offensive playersare positioned well behind the line of scrimmage, thus, suggesting a lowprobability of any present formation. However, at a second time, t0(e.g., game clock: 12:01, play clock: 13 seconds, universal clock:16:09:55), certain of the tracked players (e.g., offensive linemen andreceivers) appear to have positioned themselves proximate the line ofscrimmage, thus, suggesting a higher probability of a pro set offensiveformation. At a third time, t1 (e.g., game clock: 11:55, play clock: 07seconds, universal clock: 16:10:01), certain tracked players (e.g., thereceivers and the quarterback) move away from the line of scrimmage,thus, suggesting that a play has begun. Additional times t2 through t5may be similarly tracked as shown and used by the various engines tohypothesize the occurrence of particular events (formations, playstart/stop, penalties, etc.). The tag location data recorded at timest−1 through t5 may provide for a data stream indicating themotions/paths of the various players throughout the duration of a playperiod. It is noted that FIG. 5 does not illustrate tracking of all theplayers after t0, or the defensive team players, in order to simplifythe illustration.

The team formation engine 312 and/or play engine 318 may analyze playerdynamics of multiple players, both offensive and defensive,simultaneously in hypothesizing the possible formations, plays, etc. Forexample, as discussed briefly above, the formation engine 312 and/orplay engine 318 may apply different weights to the tag/sensor/locationdata received for each player based in part on the player's role versusthe formation models or play models, as all the individual playerdynamics may not fully correlate to a particular formation or play. Theformation engine 312 and/or play engine 318 may then analyze thedifferent models and choose the model, or set of models, that have thehighest probability of being accurate based on the weights of all thecombined inputs.

Example Dynamic Weighting by Various Engines

Various engines of the above described performance analytics system suchas the player dynamics engine, the official dynamics engine, the ballengine, the field marker engine, the team formation engine, the playengine, and the event engine, may dynamically assign and adjust variousweights to respective inputs (e.g., tag derived data, sensor deriveddata, participant location data, formation data, play data, etc.) inorder to efficiently make their respective determinations orcalculations. In some embodiments, as discussed in great detail below,one of the engines may use data or events output from one or more of theother engines when determining to assign or adjust the various weightsto the respective inputs.

For example, in one embodiment, the player dynamics engine may receive apre-snap or pre-play indication from the team formation engineindicating that the offensive and defensive players are alignedproximate the line-of-scrimmage and that a play is about to begin. Theplayer dynamics engine may determine because the players are tightlypacked in pre-snap or pre-play positions that some tags or sensors(e.g., those transmitting proximate the player's feet) may have a lowerprobability of communication effectiveness based on line-of-sightblockages due to player bodies. This determination may, in someembodiments, be confirmed by polling of the tags or sensors in questionby one or more receivers.

If the default weight (i.e., a first weight) for data for all tags andsensors of the system is 1.0, the player dynamics engine may assign aweight of 0.1 (i.e., a second weight) to the lower communicationeffectiveness tags or sensors. In an alternative embodiment, other tagsor sensors (e.g., time-of-flight sensors, accelerometers, etc.) that aredeemed more important given the poor communication of the lowercommunication effectiveness tags or sensors may be assigned a weight of1.5.

The weights of the various tags and sensors may be used by the playerdynamics engine, or other downstream engines such as the team formationengine, the play engine, or the event engine, to reduce the impact ofthe lower communication effectiveness tags or sensors (or increase theimpact of other tags or sensors) on the determinations or calculationsof such engines. In some embodiments, such weights may be used to allowthe performance analytics system to allocate resources (e.g., power,computational resources, etc.) away from lower communicationeffectiveness tags or sensors to enhance battery life or systemperformance.

In some embodiments, the low communication effectiveness of the tags orsensors may be short-lived, for example, once a play begins the playersbegin to spread out making communication with even foot attached tagseasier. In one embodiment, the player dynamics engine may receive a snapor play start indication (e.g., indication a second time if thepreviously discussed pre-snap or pre-play indication indicated a firsttime) and, in response, may assign the default weight of 1.0 to theprior low communication effectiveness tags or sensors thereby causingthe performance analytics system to consider equally the tag deriveddata or sensor derived data resulting from such tags or sensors.

In some embodiments, a receiver may derive data from a plurality of tagsand thus may transmit tag derived data having a first tag derived datacomponent (e.g., tag derived data based on blink data transmitted by afirst tag) and a second tag derived data component (e.g., tag deriveddata based on blink data transmitted by a second tag). In someembodiments, a receiver may derive data from a plurality of sensors andmay transmit sensor derived data having a first sensor derived datacomponent (e.g., sensor derived data based on a sensor data packettransmitted by a first sensor) and a second sensor derived datacomponent (e.g., sensor derived data based on a sensor data packettransmitted by a second sensor). As will be apparent to one of ordinaryskill in the art in view of this disclosure, tag or sensor weightassignments may be applied to such tag derived data components or sensorderived data components.

In one embodiment, the player dynamics engine may determine participantlocation data by assigning a first weight to a first tag derived datacomponent (e.g., blink data transmitted by feet placed tags) and asecond weight to a second tag derived data component (e.g., blink datatransmitted by shoulder pad placed tags) at a first time period (e.g.,during a pre-snap or pre-play alignment period). The player dynamicsengine may further assign a third weight to the first tag derived datacomponent (e.g., tag derived data transmitted by feet placed tags) and afourth weight to the second tag derived data component (e.g., tagderived data transmitted by shoulder pad placed tags) at a second timeperiod (e.g., during a post-snap or in-play period).

In another embodiment, the team formation engine may again determinethat offensive and defensive players are forming into pre-snap orpre-play positions. In some embodiments, the team formation engine maydetermine that the movement of certain players (e.g., a quarterback,wide receivers, runningbacks, etc.) is a better indication of pre-snapor pre-play formations than others (e.g., offensive linemen, etc.). Forexample, when analyzing player location data for comparison to formationmodels, the team formation engine may assign a first weight (e.g., 1.5)to a first participant component (e.g., location data associated with aquarterback) and a second weight (e.g., 0.1) to a second participantcomponent (e.g., location data associated with a left guard). Asdiscussed above, such weights may be assigned or adjusted by theperformance analytics system at various system times (e.g., at initialparticipant registration, pre-snap, play start, play end, etc.).

In one embodiment, the team formation engine may determine formationdata by assigning a first weight to a first participant component (e.g.,location data associated with a quarterback) and a second weight to asecond participant component (e.g., location data associated with a leftguard) at a first time period (e.g., during a pre-snap or pre-playalignment period). The team formation engine may further assign a thirdweight to the first participant component (e.g., location dataassociated with a quarterback) and a fourth weight to the secondparticipant component (e.g., location data associated with a left guard)at a second time period (e.g., during a post-snap or in-play period).

In another embodiment, the play engine may again determine thatoffensive and defensive players are forming into pre-snap or pre-playpositions. In some embodiments, the play engine may determine that themovement of certain players (e.g., a quarterback, wide receivers,runningbacks, etc.) is a better indication of plays, or play relatedevents such as play start or play stop, than others (e.g., offensivelinemen, etc.). For example, when analyzing changes in formation datafor comparison to play models, the play engine may assign a first weight(e.g., 1.5) to a first formation component (e.g., formation dataassociated with a quarterback) and a second weight (e.g., 0.1) to asecond formation component (e.g., formation data associated with a leftguard). As discussed above, such weights may be assigned or adjusted bythe performance analytics system at various system times or duringvarious periods (e.g., at initial participant registration, pre-snap,play start, play end, in-play, post-play, etc.).

As will be apparent to one of ordinary skill in the art in view of thisdisclosure, the weights of the various participant components andformation components may be used by the player dynamics engine, the teamformation engine, the play engine, or the event engine to improve theperformance analytics system. In some embodiments, such weights may beused to allow the performance analytics system to allocate resources(e.g., power, computational, etc.) away from tags, sensors, orparticipants to enhance battery life or system performance.

In another embodiment, as the team formation engine compares participantlocation data to the formation models and eliminates certain formationsfrom the list of possible formations, the team formation engine mayidentify players whose dynamics have little impact on the decision amongthe remaining possible formations and lower the weights assigned tothose particular players (e.g., tag derived data components, sensorderived data components, etc.) or may determine that the dynamics ofsome other players (e.g., tag derived data components, sensor deriveddata components, etc.) are highly relevant to certain formations and,thus, increase such other players' weights.

In another example, the official dynamics engine may receive data fromthe play engine or the formation engine and based on the type offormation or play and/or the location of the particular official inrelation to the formation or play, may recognize that the participantlocation data, formation data, event data, etc., are likely not going tobe determinative of one or more plays, formations, penalties, etc. Theplay engine or formation engine may thus assign lower weights to theparticipant location data, formation data, or event data associated withsuch official at least for a period of time (e.g., the length of oneplay, etc.).

In some embodiments, the performance and analytics system may work outrelevance data (i.e., one or more relevance factors) for variousparticipants as part of its play determination methods. For example, theperformance and analytics system may receive participant location datadetermined based on tag location data, tag/sensor derived data, andparticipant role data, wherein the participant location data comprises afirst participant component and a second participant component; receiveformation data; determine a relevance factor for each of the firstparticipant component and the second participant component based on theformation data; assign a first weighted participant component and asecond weighted participant component based on the relevance factor foreach of the first participant component and the second participantcomponent; and determine play data based on comparing the first weightedparticipant component, the second weighted participant component, andthe formation data to play models.

In one example embodiment, the location of the ball may be used todetermine a play. In this regard, the performance and analytics systemreceives ball location data; receives tag derived data, such as blinkdata transmitted by a plurality of tags, correlated to a plurality ofparticipants (e.g., players, officials, etc.); determines tag locationdata based on the blink data; receives participant role data; determinesparticipant location data based on comparing the tag location data andparticipant role data to participant dynamics/kinetics models; anddetermines play data (e.g., a play or series of plays) based on thecomparing the participant location data and the ball location data toplay models.

In another example embodiment, the location of one or more officials maybe used to determine a play. In this regard, the performance andanalytics system receives official location data; receives tag deriveddata, such as blink data transmitted by a plurality of tags, correlatedto a plurality of participants (e.g., players, other officials, etc.);determines tag location data based on the blink data; receivesparticipant role data; determines participant location data based oncomparing the tag location data and participant role data to participantdynamics/kinetics models; and determines play data (e.g., a play orseries of plays) based on the comparing the participant location dataand the official location data to play models.

Example Weather Integration Embodiments

In some embodiments, the various engines and/or output systems mayincorporate real-time weather data into the performance analytics.Outdoor sporting events are often affected by weather conditions thatmay in turn affect decisions related to carrying out the sporting eventor affect the analysis and/or prediction of a player's performance inthe sporting event.

In such embodiments, the performance analytics system may furtherinclude weather sensors such as anemometers, temperature sensors, rainsensors, or the like. In some embodiments, returning for example to FIG.1, the weather sensors may have location tags, such as location tags102, attached or incorporated thereto, so as to provide location datafor the weather sensors to the performance analytics system. In otherembodiments, the weather sensors 125 may be mounted in a fixed weatherexposed location. In the depicted embodiment, weather sensors 125 aremounted to an upper portion of the goal posts (not shown).

The weather sensors 125 may provide real-time weather data (i.e., sensorderived data from one or more weather sensors), such as wind speed, winddirection, barometric pressure, sunlight intensity, temperature,precipitation, or the like. In some embodiments where the weathersensors are integrated into tags 102, for example, location informationfor the weather sensors may be transmitted to the performance analyticssystem through wired connections or wireless connections (i.e., as tagderived data or sensor derived data, etc.), from the receivers 106. Theperformance analytics system may correlate the time and location ofparticipants (e.g., player, ball, etc.) and the weather sensor deriveddata to create weather-adjusted participant location data. Theperformance analytics system may correlate time, formation, and theweather sensor derived data to create weather-adjusted formation data.The performance analytics system may correlate the time, play data, andthe weather sensor derived data to create weather-adjusted play data. Inthis regard, the performance analytics system may determine weatherrelated impacts on participant performance, formation performance, teamperformance, output events, the trajectory of the ball, etc.

In one embodiment, the performance and analytics system may determineplayer performance information based on comparing the player locationdata and the weather sensor derived data to stored weather-adjustedplayer location data. In another embodiment, the performance andanalytics system may determine team performance information based oncomparing the formation data and the weather sensor derived data tostored weather-adjusted formation data. In still another embodiment, theperformance and analytics system may determine team performanceinformation based on comparing the play data and the weather sensorderived data to stored weather-adjusted play data.

In some embodiments, a plurality of weather sensors may be locatedproximate a monitored area or zone (i.e., a field of play). Weathersensor derived data from such plurality of sensors may be used todetermine weather conditions proximate such sensors. In this regard, theperformance and analytics system configured as described herein mayorganize such weather sensor derived data into various weather zones(e.g., horizontal zones, vertical zones, etc.) within the monitored areato better analyze the impact of weather on participant performance(e.g., player performance), formation performance, team performance,output events, and the like.

The above zone-based weather data organization may be used whendetermining how a play should be executed or modified. For example, windconditions may be different in different portions of a monitored area(e.g., a playing field) and by having knowledge of such differentweather conditions (i.e., the conditions in various monitored zones), apassing play or field goal attempt may be modified to take advantage ofconditions in each specific zone. For example, passing plays or kickingplays may be determined to be executed or not executed in certain zonesof the field based on wind conditions in such zones.

Weather sensor derived data from various weather sensors 125 may bestored to a database such as weather data store 375 of FIG. 3. Suchweather sensor derived data may be fed to event engine 322. In otherembodiments, weather sensor derived data from the weather data store 375may be accessed by the player dynamics engine 306, the official dynamicsengine 324, the ball engine 330, the team formation engine 312, the playengine 318, and the event engine 322.

In various embodiments, output events generated by one or more of theabove engines may account for, or filter out, uncontrollable weatherevents. For example, when evaluating participant performance, a “nowind” or steady-state wind trajectory for the ball may be calculated andused to determine if a kick or a pass would have been successful (i.e.,reached its target) if not for a change in weather conditions (e.g.,wind gusts, etc.). In another example, an interception or passincompletion could be determined to have been aided by wind conditions(e.g., wind gusts, etc.). Alternatively, kicks or pass completions maybe determined to have been aided by wind conditions.

Temperature, moisture, humidity, etc., can be correlated to participant,team, formation, and/or play performance (positive performance ornegative performance) to assist in arriving at more accurate evaluationsversus prior or predicted performance levels. In still anotherembodiment, such correlations may be helpful in creating more robustparticipant dynamics models, formation models, and play models.

Example Process or Method Embodiments

FIG. 6A illustrates a flowchart of an exemplary process for performanceanalytics using a locating system in accordance with some embodiments ofthe present invention. The process may start at 502, where one or moretags (e.g., tags 102 as shown in FIG. 1) may be correlated to aparticipant (e.g., a player, official, ball, etc.). Additionally, insome embodiments, one or more sensors (e.g., sensors 203 as shown inFIG. 2A-C) may be correlated to a participant at 504. The tags 102 (andoptionally sensors 203) may be attached to participants, such as toplayers, officials, balls, field markers, penalty flags, other gameequipment, and reference markers on a field of play (e.g., boundarydefining reference markers). For example, in the case of players orofficials, the tags and/or sensors may be attached to equipment,uniforms, etc., worn or carried by the players or officials.

At 506, blink data is received from the one or more tags 102.Additionally, in some embodiments, other tag derived data and sensorderived data, such as from sensors 203 associated with the participant,may be received with the blink data or separate from the blink data at508.

At 510, tag location data is determined (e.g., perhaps by receiverhub/locate engine 302) from the blink data as discussed above. Role datafor the participant is received at step 512.

In some embodiments, each participant may be associated with one or moretags 102 and/or one or more sensors 203 (e.g., multiple tags 102 andsensors 203 may be attached to an individual player's equipment, such asto provide more accurate location and multi-dimensional location ororientation data). A filter (e.g., tag ID/filter 304 of FIG. 3) mayprocess the incoming stream of tag location data to identify tags 102that are associated with a given participant (e.g., multiple tagsattached to a player, a ball, an official, etc.). The filter maycorrelate the tag location data associated with multiple tags 102 wherethe multiple tags 102 are associated with the same participant (e.g.,player or official), such as to provide more accurate data regarding theactivities of a participant. Once the tag location data is correlated toa given participant, it may be routed to an appropriate engine (e.g.,player dynamics engine, official dynamics engine, ball engine, fieldmarker engine, etc.) based at least in part on the received role dataand such correlation. Additionally, in some embodiments, sensor deriveddata from multiple sensors 203 that are associated with a givenparticipant may be correlated in a similar fashion.

In embodiments where the tag location data is routed to the playerdynamics engine, the player dynamics engine (e.g., player dynamicsengine 306 of FIG. 3) may receive the stream of participant correlatedtag derived data, and optionally, other tag/sensor derived data, fromthe filter. In other embodiments, depending on the type of participant,the below process may be performed by other appropriate engines such asthe official dynamics engine, the ball engine, the field marker engine,etc.

At 514, the player dynamics engine may compare the tag derived data andthe received player role data to a plurality of player dynamics/kineticsmodels to determine player dynamics (e.g., multi-dimensional playerlocation information) for each participant (e.g., player). Additionally,in some embodiments, the received sensor derived data may be used in thecomparison to a plurality of player dynamics/kinetics models todetermine player dynamics at 516.

At 518, the player dynamics engine may determine player location datafor each player (e.g., player dynamics or multi-dimensional playerlocation information), such as location, change in location,orientation, velocity, acceleration, deceleration, or the like. Theplayer dynamics engine may then provide an output stream of the playerlocation data, such as to a team formation engine, a play engine, anevent engine, or the like.

FIG. 6B illustrates a flowchart of another exemplary process forperformance analytics using a locating system in accordance with someembodiments of the present invention. The process may start at 520,where one or more tags (e.g., tags 102) may be correlated to aparticipant (e.g., a player, official, ball, etc.). Additionally, insome embodiments, one or more sensors (e.g., sensors 203) may becorrelated to a participant at 522.

At 524, blink data is received from the one or more tags 102.Additionally, in some embodiments, other tag derived data and sensorderived data, such as from sensors 203 associated with the participant,may be received with the blink data or separate from the blink data at528. Tag location data is determined (e.g., perhaps by receiverhub/locate engine 302) from the blink data at step 526.

At 530, a player dynamics engine may receive tag derived data for thetags 102 where the tag derived data may be indicative of a playerlocation (e.g., as opposed to an official location, a field markerlocation, etc.). Additionally, in some embodiments, other tag and sensorderived data, such as from sensors 203 associated with the player, maybe received with the blink data or separate from the blink data at 528.

In some embodiments, at 530, the player dynamics engine may optionallyreceive player role data for the player, such as by comparing a tagidentifier of the tag derived data to a database of player roles.

At 532, the player dynamics engine may then compare the tag derived data(and optionally the player role data) to a plurality of playerdynamics/kinetics models to determine player dynamics (e.g.,multi-dimensional player location information) for each player.Additionally, in some embodiments, the received sensor derived data maybe used in the comparison to a plurality of player dynamics/kineticsmodels to determine player dynamics at 534.

At 536, the player dynamics engine may determine player location datafor each player, such as location, change in location, orientation,velocity, acceleration, deceleration, or the like.

At 538, the player role data may be created or updated, such as in aplayer role database, based on the player location data. For example, ifparticipant role data for the particular participant already exists in aparticipant role database, the participant role data may be updated orchanged based on an analysis of the participant location data. Ifparticipant role data for a particular participant does not exist in theparticipant role database, a new participant role data entry may becreated for that particular participant and stored to the database. Assuch, the performance analytics system may learn about participant rolesas a result of analyzing the participant dynamics (participant locationdata).

In some embodiments, the participant role data (e.g., player role data)may comprise participant profile data such as the role of theparticipant in the game or sporting event (e.g., what position a playeris assigned), biometric data, participant analysis data, team ID,performance statistics, and/or the like. For example, the player roledata may additionally include data regarding a player's normal gait, thepattern a player typically runs, how long on average it takes a playerto start from a line of scrimmage, etc. Some embodiments may learn andupdate one or more portions of the player role data based on theanalysis of the participant location data. For example, the performanceanalytics system may identify that a player's assigned position may havechanged based on the changes in the player location data and the playerdynamics, or the system may identify a player's typical gait or typicalrunning pattern by analyzing the player location data (and/or othertag/sensor derived data), and then update the player role dataaccordingly.

FIG. 7 illustrates a flowchart of an exemplary process for a playerdynamics engine (e.g., player dynamics engine 306 of FIG. 3) inaccordance with some embodiments of the present invention. The processmay start at 602, where tag location data is received for tags 102. Insome embodiments, such tag location data may be determined by a receiverhub/locate engine 302 based on blink data transmitted by the tags 102.Additionally, in some embodiments, other tag and sensor derived data,such as from sensors 203, may be received with the tag location data orseparately from the tag location data. At 604, the player dynamicsengine may retrieve player role data from a database based on the tag ID(or participant ID) of the tag derived data. At 606, the player dynamicsengine may use the player role data, player dynamics/kinetics models(e.g., from one or more databases of player dynamics/kinetics models),the tag location data, and, optionally, the other tag derived dataand/sensor derived data to determine player dynamics (e.g.,multi-dimensional player location information) for each particularplayer, such as location, change in location, velocity, acceleration,deceleration, orientation, or the like. At 608, the player dynamicsengine may provide an output stream of the player dynamics over time(e.g., participant location data), such as to a team formation engine, aplay engine, an event engine, or the like.

FIG. 8 illustrates a flowchart of an exemplary process for a teamformation engine (e.g., team formation engine 312 of FIG. 3) inaccordance with some embodiments of the present invention. The processmay start at 702, where a player dynamics data stream (e.g., playerlocation data) is received (e.g., from a player dynamics engine), whichmay comprise blink data, tag location data, sensor data, and otherplayer dynamics data for a plurality of players. At 704, the teamformation engine may retrieve field data and formation models from oneor more databases and compare the player dynamics data stream, inconjunction with the field data, to the plurality of formation models.The team formation engine may analyze the data stream of player dynamicsover time to determine a probable formation, or set of probableformations, (e.g., the likelihood that a particular formation isoccurring or forming) at 706. For example, the team formation engine maydetermine the most probable team formation (or ranked list of probableformations) at a particular point in time. At 708, the team formationengine may provide an output stream of the formations versus time (e.g.,formation data), such as to a play engine, an event engine, or the like.

FIG. 9 illustrates a flowchart of an exemplary process for a play engine(e.g., play engine 318 of FIG. 3) in accordance with some embodiments ofthe present invention. The process may start at 802, where a playerdynamics data stream (e.g. player location data) and team formationsversus time data stream (e.g., formation data) are received (e.g., froma player dynamics engine and a team formation engine, respectively). Insome embodiments, additional data may be received, such as an officialdynamics data stream, a ball versus time data stream, a field markerdata stream, and/or the like to further improve play determinationaccuracy or assist in generating play data. At 804, the play engine mayretrieve play models from one or more databases and compare the receiveddata streams to the plurality of play models. The play engine mayanalyze the data streams in conjunction with the play models todetermine a probable play, or set of probable plays, at 806. At 808, theplay engine may analyze the data stream to determine the status of theparticular play, such as play start, in progress, play stop, or thelike. In determining that a play has formed, started, ended, etc., theplay engine may weigh and analyze the received data streams and compareto the play models to generate a ranked list of one or more probableplay events and include an associated probability that the received datamatches each particular model or pattern. At 810, the play engine mayprovide an output stream of the plays versus time (e.g., play data),such as to an event engine, or the like.

FIG. 10 illustrates a flowchart of an exemplary process for an eventengine (e.g., event engine 322 of FIG. 3) in accordance with someembodiments of the present invention. The process may start at 902,where a player dynamics data stream (e.g., player location data), teamformations versus time data stream (e.g., formation data), and a playsversus time data stream (e.g., play data) are received (e.g., from aplayer dynamics engine, a team formation engine, and a play engine,respectively). In some embodiments, additional data streams may bereceived, such as an official dynamics data stream, a ball versus timedata stream, a field marker data stream, a weather data stream, and/orthe like to assist in generating event data streams. At 904, the eventengine may process the received data streams to determine and generateevents during, or in conjunction with, a game. At 906, the event enginemay provide output streams of the event data to various storage,analysis, and/or control systems, such as, without limitation, to ahistorical data store 336, a visualization system 340, a game operationssystem 342, a camera control system 344, a team analytics system 346, aleague analytics system 348, a statistics system 350, an XML feed/IMfeed system, and/or the like.

FIG. 11 illustrates a flowchart of an exemplary process for teamanalysis, such as by a team analytics system (e.g., team analyticssystem 346 of FIG. 3), in accordance with some embodiments of thepresent invention. The process may start at 1002, where input play data(e.g., future/potential play models) for a particular team may bereceived, such as through a model generation engine 338. At 1004, theteam analytics system may determine a particular type of analysis to beperformed using the input play data. If a selection is made to comparethe input plays against the play models in the system (e.g., play modeldatabases), at 1006, the team analytics system may compare the inputplays against each of the appropriate play models (i.e., offensiveplays, defensive plays, etc.) in the play model database, such as usingperformance data associated with the play models from historical datastore 336. If a selection is made to compare the input plays against theformation models in the system, at 1008, the team analytics system maycompare the input plays against each of the appropriate formations foreach of the appropriate play models (i.e., offensive plays, defensiveplays, etc.) in the formation and play model databases, such as usingperformance data associated with the formation and play models fromhistorical data store 336. If a selection is made to compare the inputplays against the historical play data of another team, at 1010, theteam analytics system may compare the input plays against each of theappropriate play models used by the other team, such as usingperformance data associated with the play models from historical datastore 336. At 1012, the team analytics system may output the results ofthe analysis.

FIG. 12 illustrates a flowchart of an exemplary process for playeranalysis, such as by a team analytics system, in accordance with someembodiments of the present invention. The process may start at 1102,where a user may input a selection of a player for analysis. At 1104,the team analytics system may retrieve historical player data and/orstatistics for the selected player, such as from historical data store336. At 1106, the team analytics system may determine the selection of aparticular type of analysis to be performed for the selected player. Ifa selection is made to compare the player's performance against the playmodels in the system, at 1108, the team analytics system may compare theplayer performance against each of the appropriate play models (i.e.,offensive plays, defensive plays, etc.) in the play model database, suchas using performance data of the play models by the player's team fromthe historical data store 336. If a selection is made to compare playerperformance against the formation models in the system, at 1110, theteam analytics system may compare the player performance against each ofthe appropriate formations for each of the appropriate play models(i.e., offensive plays, defensive plays, etc.) in the formation and playmodel databases, such as using historical performance data of theformation and play models by the player's team from the historical datastore 336. If a selection is made to compare the player performanceagainst the historical play data of another team, at 1012, the teamanalytics system may compare the player performance against each of theappropriate play models used by the other team, such as using historicalperformance data of the play models from the historical data store 336.At 1114, the team analytics system may output the results of theanalysis.

Example Processing Apparatus

FIG. 13 shows a block diagram of components that may be included in anapparatus that may provide performance analytics in accordance withembodiments discussed herein. Apparatus 1200 may comprise one or moreprocessors, such as processor 1202, one or more memories, such as memory1204, communication circuitry 1206, and user interface 1208. Processor1202 can be, for example, a microprocessor that is configured to executesoftware instructions and/or other types of code portions for carryingout defined steps, some of which are discussed herein. Processor 1202may communicate internally using data bus, for example, which may beused to convey data, including program instructions, between processor1202 and memory 1204.

Memory 1204 may include one or more non-transitory storage media suchas, for example, volatile and/or non-volatile memory that may be eitherfixed or removable. Memory 1204 may be configured to store information,data, applications, instructions or the like for enabling apparatus 1200to carry out various functions in accordance with example embodiments ofthe present invention. For example, the memory could be configured tobuffer input data for processing by processor 1202. Additionally oralternatively, the memory could be configured to store instructions forexecution by processor 1202. Memory 1204 can be considered primarymemory and be included in, for example, RAM or other forms of volatilestorage which retain its contents only during operation, and/or memory1204 may be included in non-volatile storage, such as ROM, EPROM,EEPROM, FLASH, or other types of storage that retain the memory contentsindependent of the power state of the apparatus 1200. Memory 1204 couldalso be included in a secondary storage device, such as external diskstorage, that stores large amounts of data. In some embodiments, thedisk storage may communicate with processor 1202 using an input/outputcomponent via a data bus or other routing component. The secondarymemory may include a hard disk, compact disk, DVD, memory card, or anyother type of mass storage type known to those skilled in the art.

In some embodiments, processor 1202 may be configured to communicatewith external communication networks and devices using communicationscircuitry 1206, and may use a variety of interfaces such as datacommunication oriented protocols, including X.25, ISDN, DSL, amongothers. Communications circuitry 1206 may also incorporate a modem forinterfacing and communicating with a standard telephone line, anEthernet interface, cable system, and/or any other type ofcommunications system. Additionally, processor 1202 may communicate viaa wireless interface that is operatively connected to communicationscircuitry 1206 for communicating wirelessly with other devices, usingfor example, one of the IEEE 802.11 protocols, 802.15 protocol(including Bluetooth, Zigbee, and others), a cellular protocol (AdvancedMobile Phone Service or “AMPS”), Personal Communication Services (PCS),or a standard 3G wireless telecommunications protocol, such as CDMA20001x EV-DO, GPRS, W-CDMA, LTE, and/or any other protocol.

The apparatus 1200 may include a user interface 1208 that may, in turn,be in communication with the processor 1202 to provide output to theuser and to receive input. For example, the user interface may include adisplay and, in some embodiments, may also include a keyboard, a mouse,a joystick, a touch screen, touch areas, soft keys, a microphone, aspeaker, or other input/output mechanisms. The processor may compriseuser interface circuitry configured to control at least some functionsof one or more user interface elements such as a display and, in someembodiments, a speaker, ringer, microphone and/or the like. Theprocessor and/or user interface circuitry comprising the processor maybe configured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory 1204, and/or the like).

In some embodiments, certain ones of the operations above may bemodified or further amplified as described below. Moreover, in someembodiments additional optional operations may also be included. Itshould be appreciated that each of the modifications, optional additionsor amplifications below may be included with the operations above eitheralone or in combination with any others among the features describedherein.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe example embodiments in the context of certain examplecombinations of elements and/or functions, it should be appreciated thatdifferent combinations of elements and/or functions may be provided byalternative embodiments without departing from the scope of the appendedclaims. In this regard, for example, different combinations of elementsand/or functions than those explicitly described above are alsocontemplated as may be set forth in some of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

That which is claimed:
 1. A method for monitoring at least twoparticipants, the method comprising: receiving first tag location datafor a first participant during a first time period; receiving subsequentfirst tag location data for the first participant during a second timeperiod; receiving second tag location data for a second participantduring the first time period; and determining subsequent secondparticipant location data for the second participant during the secondtime period based at least on: the first tag location data for the firstparticipant during the first time period, the subsequent first taglocation data for the first participant during the second time period,and the second tag location data for the second participant during thefirst time period.
 2. The method of claim 1 further comprising:receiving first sensor derived data for the first participant during thefirst time period; receiving subsequent first sensor derived data forthe first participant during the second time period; and wherein thedetermining the subsequent second participant location data for thesecond participant during the second time period is further based atleast on: the first sensor derived data for the first participant duringthe first time period, and the subsequent first sensor derived data forthe first participant during the second time period.
 3. The method ofclaim 2, wherein the first sensor derived data and the subsequent firstsensor derived data indicate a relative proximity between the firstparticipant and the second participant.
 4. The method of claim 1 furthercomprising: receiving second tag location data for a third participantduring the first time period; receiving subsequent second tag locationdata for the third participant during the second time period; andwherein the determining the subsequent second participant location datafor the second participant during the second time period is furtherbased at least on: subsequent second tag location data for the thirdparticipant during the second time period.
 5. The method of claim 1,wherein the determining the subsequent second participant location datafor the second participant during the second time period is furtherbased at least on play models.
 6. The method of claim 1, wherein thedetermining the subsequent second participant location data for thesecond participant during the second time period is further based atleast on formation models.
 7. The method of claim 1, wherein thedetermining the subsequent second participant location data for thesecond participant during the second time period is further based on oneor more of field data, participant role data, formation models, and playmodels.
 8. An apparatus comprising at least one processor and at leastone memory including computer program code, the at least one memory andcomputer program code configured to, with the processor, cause theapparatus to at least: receive first tag location data for a firstparticipant during a first time period; receive subsequent first taglocation data for the first participant during a second time period;receive second tag location data for a second participant during thefirst time period; and determine subsequent second participant locationdata for the second participant during the second time period based atleast on: the first tag location data for the first participant duringthe first time period, the subsequent first tag location data for thefirst participant during the second time period, and the second taglocation data for the second participant during the first time period.9. The apparatus of claim 8, wherein the at least one memory and thecomputer program code are further configured to, with the processor,cause the apparatus to at least: receive first sensor derived data forthe first participant during the first time period; receive subsequentfirst sensor derived data for the first participant during the secondtime period; and wherein the determining the subsequent secondparticipant location data for the second participant during the secondtime period is further based at least on: the first sensor derived datafor the first participant during the first time period, and thesubsequent first sensor derived data for the first participant duringthe second time period.
 10. The apparatus of claim 9, wherein the firstsensor derived data and the subsequent first sensor derived dataindicate a relative proximity between the first participant and thesecond participant.
 11. The apparatus of claim 8, wherein the at leastone memory and the computer program code are further configured to, withthe processor, cause the apparatus to at least: receive second taglocation data for a third participant during the first time period;receive subsequent second tag location data for the third participantduring the second time period; and wherein the determining thesubsequent second participant location data for the second participantduring the second time period is further based at least on: subsequentsecond tag location data for the third participant during the secondtime period.
 12. The apparatus of claim 8, wherein the determining thesubsequent second participant location data for the second participantduring the second time period is further based at least on play models.13. The apparatus of claim 8, wherein the determining the subsequentsecond participant location data for the second participant during thesecond time period is further based at least on formation models. 14.The apparatus of claim 8, wherein the determining the subsequent secondparticipant location data for the second participant during the secondtime period is further based on one or more of field data, participantrole data, formation models, and play models.
 15. A computer programproduct comprising at least one non-transitory computer-readable storagemedium having computer-executable program code portions stored therein,the computer-executable program code portions comprising program codeinstructions configured to: receive first tag location data for a firstparticipant during a first time period; receive subsequent first taglocation data for the first participant during a second time period;receive second tag location data for a second participant during thefirst time period; and determine subsequent second participant locationdata for the second participant during the second time period based atleast on: the first tag location data for the first participant duringthe first time period, the subsequent first tag location data for thefirst participant during the second time period, and the second taglocation data for the second participant during the first time period.16. The computer program product of claim 15, wherein thecomputer-executable program code portions further comprise program codeinstructions configured to: receive first sensor derived data for thefirst participant during the first time period; receive subsequent firstsensor derived data for the first participant during the second timeperiod; and wherein the determining the subsequent second participantlocation data for the second participant during the second time periodis further based at least on: the first sensor derived data for thefirst participant during the first time period, and the subsequent firstsensor derived data for the first participant during the second timeperiod.
 17. The computer program product of claim 16, wherein the firstsensor derived data and the subsequent first sensor derived dataindicate a relative proximity between the first participant and thesecond participant.
 18. The computer program product of claim 15,wherein the determining the subsequent second participant location datafor the second participant during the second time period is furtherbased at least on play models.
 19. The computer program product of claim15, wherein the determining the subsequent second participant locationdata for the second participant during the second time period is furtherbased at least on formation models.
 20. The computer program product ofclaim 15, wherein the determining the subsequent second participantlocation data for the second participant during the second time periodis further based on one or more of field data, participant role data,formation models, and play models.